Modeling the Effect of Variation of Recruitment Rate on the Transmission Dyna...IOSR Journals
In this Paper, the effect of the variation of recruitment rate on the transmission dynamics of
tuberculosis was studied by modifying an existing model. While the recruitment rate into the susceptible class of
the existing model is constant, in our modified model we used a varying recruitment rate. The models were
analyzed analytically and numerically and these results were compared. The Disease Free Equilibrium (DFE)
state of the existing model was found to be
,0,0,0
, the DFE of the modified model was found to be
( ,0,0,0) * S where * S is arbitrary. While all the eigenvalue of the existing model are negative, one of the
eigenvalues of the modified model is zero. The basic reproduction number o R of both models are established to
be the same. The numerical experiments show a gradual decline in the infected and exposed populations as the
recruitment rates increase in both models but the decline is more in the modified model than in the existing
model. This implies that eradication will be achieved faster using the model with a varying recruitment rate.
This paper proposes a vaccine-dependent mathematical model to study the transmission dynamics of tuberculosis (TB) epidemics at the population level. The model divides the population into susceptible, latently infected unvaccinated, latently infected vaccinated, actively infected, recovered, and vaccinated classes. The paper proves the existence and uniqueness of a solution to the system of equations that defines the model. It also shows that the infection will die out if the basic reproduction number is less than one. The model could be used to estimate new TB infections and help design prevention and intervention strategies.
Analysis And Modeling Of Tuberculosis Transmission DynamicsSara Alvarez
This document presents a mathematical model for analyzing the transmission dynamics of tuberculosis (TB). The model divides the population into four compartments: susceptible individuals, infectious individuals, latently infected individuals, and recovered individuals. Ordinary differential equations are used to model the flow of individuals between compartments. The model's disease-free equilibrium and stability are analyzed. Sensitivity analysis determines that recruitment rate and contact rate are the most sensitive parameters affecting the basic reproduction number. The findings show that as contact with infectious individuals increases, TB spread increases, and the latent infection rate must remain below a critical value for TB to persist in the population.
- The document describes a thesis that examines resource allocation strategies for controlling infectious disease outbreaks.
- It analyzes outbreaks from natural causes like influenza, bioterrorist attacks like smallpox, and outbreaks in humanitarian emergencies like cholera.
- Mathematical models are developed and coupled with epidemiological models to optimize allocation of resources like medical teams during vaccination campaigns. A case study applies the models to influenza outbreaks in Greece.
Methodological Challenges in Evaluating Malaria Control Program Impact: How d...MEASURE Evaluation
Presented by Tom Smith, Swiss Tropical and Public Health Institute, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.
This document discusses developing tuberculosis (TB) diagnostics using deep learning and mobile health technologies. It aims to reduce delays in TB diagnosis among resource-poor communities. The researchers created a large chest X-ray image database with TB annotations and developed deep learning models to classify images. They plan to deploy the system in Peru to automatically screen chest X-rays using mobile devices and cloud computing. This could help accelerate TB diagnosis and reduce transmission in areas with weak healthcare systems.
An Epidemiological Model of Malaria Transmission in Ghana.pdfEmily Smith
- The document presents an epidemiological model of malaria transmission in Ghana using a system of ordinary differential equations with human and mosquito populations.
- The model divides the human population into susceptible, exposed, infectious, and recovered categories and the mosquito population into susceptible, exposed, and infectious categories.
- Sensitivity analysis found that the mosquito biting rate and mosquito death rate were the most sensitive parameters affecting the basic reproduction number. Simulations showed that combining four control measures - insecticide spraying, bed net usage, and treatment of infected humans and pregnant women - had the highest impact on reducing disease transmission.
ciclo autonomico-short paper - Witfor 2016 paper_42.. ..
This paper presents an ongoing project to develop a biocomputational platform to analyze genomic data from cancer patients and bacteria in Costa Rica. The platform will integrate genomic data processing, prediction of drug sensitivity, and identification of new therapeutic targets. It will use pattern recognition techniques and mathematical models on genomic and drug response data to predict personalized therapy. Preliminary results include databases to store cancer and bacteria genomic data, and tools for exploring relationships between genomic features and drug responses. The platform aims to help identify optimal personalized treatments to overcome drug resistance in cancer and bacterial infections.
Modeling the Effect of Variation of Recruitment Rate on the Transmission Dyna...IOSR Journals
In this Paper, the effect of the variation of recruitment rate on the transmission dynamics of
tuberculosis was studied by modifying an existing model. While the recruitment rate into the susceptible class of
the existing model is constant, in our modified model we used a varying recruitment rate. The models were
analyzed analytically and numerically and these results were compared. The Disease Free Equilibrium (DFE)
state of the existing model was found to be
,0,0,0
, the DFE of the modified model was found to be
( ,0,0,0) * S where * S is arbitrary. While all the eigenvalue of the existing model are negative, one of the
eigenvalues of the modified model is zero. The basic reproduction number o R of both models are established to
be the same. The numerical experiments show a gradual decline in the infected and exposed populations as the
recruitment rates increase in both models but the decline is more in the modified model than in the existing
model. This implies that eradication will be achieved faster using the model with a varying recruitment rate.
This paper proposes a vaccine-dependent mathematical model to study the transmission dynamics of tuberculosis (TB) epidemics at the population level. The model divides the population into susceptible, latently infected unvaccinated, latently infected vaccinated, actively infected, recovered, and vaccinated classes. The paper proves the existence and uniqueness of a solution to the system of equations that defines the model. It also shows that the infection will die out if the basic reproduction number is less than one. The model could be used to estimate new TB infections and help design prevention and intervention strategies.
Analysis And Modeling Of Tuberculosis Transmission DynamicsSara Alvarez
This document presents a mathematical model for analyzing the transmission dynamics of tuberculosis (TB). The model divides the population into four compartments: susceptible individuals, infectious individuals, latently infected individuals, and recovered individuals. Ordinary differential equations are used to model the flow of individuals between compartments. The model's disease-free equilibrium and stability are analyzed. Sensitivity analysis determines that recruitment rate and contact rate are the most sensitive parameters affecting the basic reproduction number. The findings show that as contact with infectious individuals increases, TB spread increases, and the latent infection rate must remain below a critical value for TB to persist in the population.
- The document describes a thesis that examines resource allocation strategies for controlling infectious disease outbreaks.
- It analyzes outbreaks from natural causes like influenza, bioterrorist attacks like smallpox, and outbreaks in humanitarian emergencies like cholera.
- Mathematical models are developed and coupled with epidemiological models to optimize allocation of resources like medical teams during vaccination campaigns. A case study applies the models to influenza outbreaks in Greece.
Methodological Challenges in Evaluating Malaria Control Program Impact: How d...MEASURE Evaluation
Presented by Tom Smith, Swiss Tropical and Public Health Institute, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.
This document discusses developing tuberculosis (TB) diagnostics using deep learning and mobile health technologies. It aims to reduce delays in TB diagnosis among resource-poor communities. The researchers created a large chest X-ray image database with TB annotations and developed deep learning models to classify images. They plan to deploy the system in Peru to automatically screen chest X-rays using mobile devices and cloud computing. This could help accelerate TB diagnosis and reduce transmission in areas with weak healthcare systems.
An Epidemiological Model of Malaria Transmission in Ghana.pdfEmily Smith
- The document presents an epidemiological model of malaria transmission in Ghana using a system of ordinary differential equations with human and mosquito populations.
- The model divides the human population into susceptible, exposed, infectious, and recovered categories and the mosquito population into susceptible, exposed, and infectious categories.
- Sensitivity analysis found that the mosquito biting rate and mosquito death rate were the most sensitive parameters affecting the basic reproduction number. Simulations showed that combining four control measures - insecticide spraying, bed net usage, and treatment of infected humans and pregnant women - had the highest impact on reducing disease transmission.
ciclo autonomico-short paper - Witfor 2016 paper_42.. ..
This paper presents an ongoing project to develop a biocomputational platform to analyze genomic data from cancer patients and bacteria in Costa Rica. The platform will integrate genomic data processing, prediction of drug sensitivity, and identification of new therapeutic targets. It will use pattern recognition techniques and mathematical models on genomic and drug response data to predict personalized therapy. Preliminary results include databases to store cancer and bacteria genomic data, and tools for exploring relationships between genomic features and drug responses. The platform aims to help identify optimal personalized treatments to overcome drug resistance in cancer and bacterial infections.
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a mathematical model that examines the effects of vaccination against tuberculosis transmission from mother to child. The model divides the population into five groups: those immunized by the BCG vaccine, susceptible individuals, those with mild infection, those with severe infection, and recovered individuals. The model equations describe the transitions between these groups over time based on factors like natural death rate, infection rates, recovery rates, and vaccine efficacy expiration. Analysis of the model finds disease-free and endemic equilibrium states. Stability analysis determines that the endemic equilibrium will be stable if the total removal rate from the infectious class is greater than the number of latent infections produced during the infectious period. In other words, vaccination must reduce the basic reproduction number R0 to
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Non compartmental s-i-s modeling of hiv prevalence in 7 countries of the worldAlexander Decker
This document presents two non-compartmental S-I-S models developed to model HIV prevalence over time in different countries. The models were validated using HIV prevalence data from 7 countries obtained online. The models fitted the data very well, with correlation coefficients close to 1. The models can be used to determine key values for each country, such as ultimate prevalence, time of peak prevalence, and time of exhaustion. Non-compartmental S-I-S models provide a simple way to model and make predictions about HIV prevalence over time for different countries.
This document presents a mathematical model of tuberculosis (TB) that considers drug resistance to first and second line treatments. The model divides the population into six compartments: susceptible, early latent, late latent, non-isolated infectious, isolated infectious, and treated/recovered. Equations were developed to model the flow of individuals between compartments. Analysis showed the disease-free equilibrium is locally and globally asymptotically stable if the basic reproduction number is less than one. Numerical simulations suggest decreasing the rate at which immunity wanes is most effective for controlling the TB epidemic.
Test positivity – Evaluation of a new metric to assess epidemic dispersal med...Olutosin Ademola Otekunrin
Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay.
Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at
least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = 0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship
was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception –a country where isolation was not pursued , all countries showed a negative correlation between
TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and
high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.
Epidemiology is the study of the distribution and determinants of health and disease in populations. It has evolved rapidly in recent decades from focusing only on disease distribution and causation to also examining health events, treatment modalities, and health services. Modern epidemiology identifies risk factors for chronic diseases and evaluates prevention and treatment options to improve population health.
This document presents a Bayesian semiparametric framework for analyzing semicompeting risks data where the observation of time to a non-terminal event (e.g. hospital readmission) is subject to a terminal event (e.g. death). The framework models the hazards of the non-terminal and terminal events using a shared frailty illness-death model, accounting for dependence between events. It allows researchers to estimate regression parameters, characterize event dependence, and predict outcomes. The framework is applied to Medicare data on pancreatic cancer patients to investigate risks of readmission and death.
Modeling and Simulation of Spread and Effect of Malaria EpidemicWaqas Tariq
The purpose of this paper is to consider malaria infection (A) and the control of malaria (B) as the two sets of soldiers engage in a war. The principal objectives are to see if it is possible with time to reduce and eradicate malaria in our environment taking reasonable precaution. The methodology approach is to model a mathematical equation using battling method approach to find the time(t) that control malaria in our environment will conquer the malaria infection i.e. when A(t)=0. The number of provided facilities (n) for the protection of malaria is also considered and varied. The result shows that as the number of malaria control increases the control time is decreasing.
A SEIR MODEL FOR CONTROL OF INFECTIOUS DISEASESSOUMYADAS835019
This document presents a SEIR model for controlling infectious diseases with constraints. It begins with an introduction to SEIR models and their use in modeling disease transmission and testing control strategies. It then motivates the study by discussing the COVID-19 pandemic. The document outlines the basic ideas of the SEIR model and describes the compartments and parameters. It presents the optimal control problem formulated to determine vaccination strategies over time. Potential application areas and future research scope are discussed before concluding with references.
The document discusses integrated vector management (IVM) as an approach to vector-borne disease control. IVM involves understanding local vector ecology and patterns of disease transmission in order to select appropriate control methods from available options. It aims to improve cost-effectiveness and sustainability compared to traditional reliance on insecticides alone. Key elements of IVM include disease and vector surveillance, identifying and mapping local risk factors, participatory selection of control methods, monitoring and evaluation. The document outlines the steps in implementing IVM, including assessing disease burden and local resources available before developing context-specific strategies.
Services and infrastructure such as health, education, water, security etc. provided by the
government and other independent providers are usually scarce and in great demand by the public. Pressure
due to over dependence on the limited resources by the ever growing population due to the influx of internally
displaced persons into Maiduguri has resulted in great dissatisfaction and sometimes wastages of the resources.
The ultimate goal of this paper is to model hospital admissions of in-patients at the State Specialist Hospital
Maiduguri, Borno State to understand the nature of dependencies of the categories of the factors on the
available facilities in terms of length of bed occupancy using socio-demographic factors. Hospital records of
1418 of in-patients who were diagnosed, admitted, treated and officially discharged from 2011-2015 were
studied and modeled using descriptive statistics and Generalized Poisson regression. The results obtained
shows clearly how the services are demanded and consumed by the different categories of the variables
considered. The results showed that gender differences, employment and age categories have significant impact
on the admission rate and the length of stay by patients on admission.
A Review On Mathematical Modeling Of Infectious DiseasesSara Alvarez
This document reviews the use of mathematical modeling to study infectious disease transmission and dynamics. It discusses how compartmental models have been widely used since the early 20th century to understand disease spread and identify key factors like reproduction numbers. The review outlines some of the earliest and most influential disease models, and describes how models have become more advanced over time by incorporating additional real-world factors. Compartmental models like SIR are commonly employed to predict outbreaks and guide control strategies.
The Susceptible-Infectious Model of Disease Expansion Analyzed Under the Scop...cscpconf
This paper presents a model to approach the dynamics of infectious diseases expansion. Our
model aims to establish a link between traditional simulation of the Susceptible-Infectious (SI)
model of disease expansion based on ordinary differential equations (ODE), and a very simple
approach based on both connectivity between people and elementary binary rules that define
the result of these contacts. The SI deterministic compartmental model has been analysed and
successfully modelled by our method, in the case of 4-connected neighbourhood.
THE SUSCEPTIBLE-INFECTIOUS MODEL OF DISEASE EXPANSION ANALYZED UNDER THE SCOP...csandit
This paper presents a model to approach the dynamics of infectious diseases expansion. Our model aims to establish a link between traditional simulation of the Susceptible-Infectious (SI) model of disease expansion based on ordinary differential equations (ODE), and a very simple approach based on both connectivity between people and elementary binary rules that define the result of these contacts. The SI deterministic compartmental model has been analysed and successfully modelled by our method, in the case of 4-connected neighbourhood.
Epidemiological method of research, structure & Maintenance. Eneutron
Epidemiology is the study of disease patterns in populations and uses a systematic method of research to identify risk factors and determine preventive measures. This document discusses the epidemiological method, epidemiological diagnostics, and the system of epidemiological surveillance. The epidemiological method involves descriptive, analytical, and experimental techniques to study disease occurrence and justify prevention. Epidemiological diagnostics provides data to support preventive actions by describing disease manifestations, risk groups, determining causes, and formulating hypotheses. Epidemiological surveillance is the ongoing assessment of disease trends to enable timely intervention through prevention and control programs.
The SIR Model and the 2014 Ebola Virus Disease Outbreak in Guinea, Liberia an...CSCJournals
This document presents a mathematical model using the SIR (Susceptible, Infected, Recovered) model to understand the spread of the 2014 Ebola virus disease outbreak in Guinea, Liberia, and Sierra Leone. The model divides the population into compartments based on disease status. Differential equations are formulated and numerically solved using data from the outbreak. The results show that initially the number of infected individuals increases, reaches a peak, and then decreases as individuals recover or die, indicating the outbreak could be controlled. Public health interventions that reduce transmission rates can help an outbreak die out by lowering the reproduction number below 1.
This document presents a mathematical model analyzing the global stability of the endemic equilibrium of HIV/AIDS with a drug-resistant compartment. The model divides the total population into five compartments: susceptible individuals, infectious individuals not receiving treatment, infectious individuals receiving treatment who are drug resistant, infectious individuals receiving treatment who are not drug resistant, and AIDS individuals. The basic reproduction number is calculated using next generation matrix methods. Analysis using Lasalle's invariant principle finds that the endemic equilibrium of the model is globally asymptotically stable.
2024 HIPAA Compliance Training Guide to the Compliance OfficersConference Panel
Join us for a comprehensive 90-minute lesson designed specifically for Compliance Officers and Practice/Business Managers. This 2024 HIPAA Training session will guide you through the critical steps needed to ensure your practice is fully prepared for upcoming audits. Key updates and significant changes under the Omnibus Rule will be covered, along with the latest applicable updates for 2024.
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https://conferencepanel.com/conference/hipaa-training-for-the-compliance-officer-2024-updates
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a mathematical model that examines the effects of vaccination against tuberculosis transmission from mother to child. The model divides the population into five groups: those immunized by the BCG vaccine, susceptible individuals, those with mild infection, those with severe infection, and recovered individuals. The model equations describe the transitions between these groups over time based on factors like natural death rate, infection rates, recovery rates, and vaccine efficacy expiration. Analysis of the model finds disease-free and endemic equilibrium states. Stability analysis determines that the endemic equilibrium will be stable if the total removal rate from the infectious class is greater than the number of latent infections produced during the infectious period. In other words, vaccination must reduce the basic reproduction number R0 to
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Non compartmental s-i-s modeling of hiv prevalence in 7 countries of the worldAlexander Decker
This document presents two non-compartmental S-I-S models developed to model HIV prevalence over time in different countries. The models were validated using HIV prevalence data from 7 countries obtained online. The models fitted the data very well, with correlation coefficients close to 1. The models can be used to determine key values for each country, such as ultimate prevalence, time of peak prevalence, and time of exhaustion. Non-compartmental S-I-S models provide a simple way to model and make predictions about HIV prevalence over time for different countries.
This document presents a mathematical model of tuberculosis (TB) that considers drug resistance to first and second line treatments. The model divides the population into six compartments: susceptible, early latent, late latent, non-isolated infectious, isolated infectious, and treated/recovered. Equations were developed to model the flow of individuals between compartments. Analysis showed the disease-free equilibrium is locally and globally asymptotically stable if the basic reproduction number is less than one. Numerical simulations suggest decreasing the rate at which immunity wanes is most effective for controlling the TB epidemic.
Test positivity – Evaluation of a new metric to assess epidemic dispersal med...Olutosin Ademola Otekunrin
Epidemic control may be hampered when the percentage of asymptomatic cases is high. Seeking remedies for this problem, test positivity was explored between the first 60 to 90 epidemic days in six countries that reported their first COVID-19 case between February and March 2020: Argentina, Bolivia, Chile, Cuba, Mexico, and Uruguay.
Test positivity (TP) is the percentage of test-positive individuals reported on a given day out of all individuals tested the same day. To generate both country-specific and multi-country information, this study was implemented in two stages. First, the epidemiologic data of the country infected last (Uruguay) were analyzed. If at
least one TP-related analysis yielded a statistically significant relationship, later assessments would investigate the six countries. The Uruguayan data indicated (i) a positive correlation between daily TP and daily new cases (r = 0.75); (ii) a negative correlation between TP and the number of tests conducted per million inhabitants (TPMI, r = 0.66); and (iii) three temporal stages, which differed from one another in both TP and TPMI medians (p < 0.01) and, together, revealed a negative relationship between TPMI and TP. No significant relationship
was found between TP and the number of active or recovered patients. The six countries showed a positive correlation between TP and the number of deaths/million inhabitants (DMI, r = 0.65, p < 0.01). With one exception –a country where isolation was not pursued , all countries showed a negative correlation between
TP and TPMI (r = 0.74). The temporal analysis of country-specific policies revealed four patterns, characterized by: (1) low TPMI and high DMI, (2) high TPMI and low DMI; (3) an intermediate pattern, and (4) high TPMI and
high DMI. Findings support the hypothesis that test positivity may guide epidemiologic policy-making, provided that policy-related factors are considered and high-resolution geographical data are utilized.
Epidemiology is the study of the distribution and determinants of health and disease in populations. It has evolved rapidly in recent decades from focusing only on disease distribution and causation to also examining health events, treatment modalities, and health services. Modern epidemiology identifies risk factors for chronic diseases and evaluates prevention and treatment options to improve population health.
This document presents a Bayesian semiparametric framework for analyzing semicompeting risks data where the observation of time to a non-terminal event (e.g. hospital readmission) is subject to a terminal event (e.g. death). The framework models the hazards of the non-terminal and terminal events using a shared frailty illness-death model, accounting for dependence between events. It allows researchers to estimate regression parameters, characterize event dependence, and predict outcomes. The framework is applied to Medicare data on pancreatic cancer patients to investigate risks of readmission and death.
Modeling and Simulation of Spread and Effect of Malaria EpidemicWaqas Tariq
The purpose of this paper is to consider malaria infection (A) and the control of malaria (B) as the two sets of soldiers engage in a war. The principal objectives are to see if it is possible with time to reduce and eradicate malaria in our environment taking reasonable precaution. The methodology approach is to model a mathematical equation using battling method approach to find the time(t) that control malaria in our environment will conquer the malaria infection i.e. when A(t)=0. The number of provided facilities (n) for the protection of malaria is also considered and varied. The result shows that as the number of malaria control increases the control time is decreasing.
A SEIR MODEL FOR CONTROL OF INFECTIOUS DISEASESSOUMYADAS835019
This document presents a SEIR model for controlling infectious diseases with constraints. It begins with an introduction to SEIR models and their use in modeling disease transmission and testing control strategies. It then motivates the study by discussing the COVID-19 pandemic. The document outlines the basic ideas of the SEIR model and describes the compartments and parameters. It presents the optimal control problem formulated to determine vaccination strategies over time. Potential application areas and future research scope are discussed before concluding with references.
The document discusses integrated vector management (IVM) as an approach to vector-borne disease control. IVM involves understanding local vector ecology and patterns of disease transmission in order to select appropriate control methods from available options. It aims to improve cost-effectiveness and sustainability compared to traditional reliance on insecticides alone. Key elements of IVM include disease and vector surveillance, identifying and mapping local risk factors, participatory selection of control methods, monitoring and evaluation. The document outlines the steps in implementing IVM, including assessing disease burden and local resources available before developing context-specific strategies.
Services and infrastructure such as health, education, water, security etc. provided by the
government and other independent providers are usually scarce and in great demand by the public. Pressure
due to over dependence on the limited resources by the ever growing population due to the influx of internally
displaced persons into Maiduguri has resulted in great dissatisfaction and sometimes wastages of the resources.
The ultimate goal of this paper is to model hospital admissions of in-patients at the State Specialist Hospital
Maiduguri, Borno State to understand the nature of dependencies of the categories of the factors on the
available facilities in terms of length of bed occupancy using socio-demographic factors. Hospital records of
1418 of in-patients who were diagnosed, admitted, treated and officially discharged from 2011-2015 were
studied and modeled using descriptive statistics and Generalized Poisson regression. The results obtained
shows clearly how the services are demanded and consumed by the different categories of the variables
considered. The results showed that gender differences, employment and age categories have significant impact
on the admission rate and the length of stay by patients on admission.
A Review On Mathematical Modeling Of Infectious DiseasesSara Alvarez
This document reviews the use of mathematical modeling to study infectious disease transmission and dynamics. It discusses how compartmental models have been widely used since the early 20th century to understand disease spread and identify key factors like reproduction numbers. The review outlines some of the earliest and most influential disease models, and describes how models have become more advanced over time by incorporating additional real-world factors. Compartmental models like SIR are commonly employed to predict outbreaks and guide control strategies.
The Susceptible-Infectious Model of Disease Expansion Analyzed Under the Scop...cscpconf
This paper presents a model to approach the dynamics of infectious diseases expansion. Our
model aims to establish a link between traditional simulation of the Susceptible-Infectious (SI)
model of disease expansion based on ordinary differential equations (ODE), and a very simple
approach based on both connectivity between people and elementary binary rules that define
the result of these contacts. The SI deterministic compartmental model has been analysed and
successfully modelled by our method, in the case of 4-connected neighbourhood.
THE SUSCEPTIBLE-INFECTIOUS MODEL OF DISEASE EXPANSION ANALYZED UNDER THE SCOP...csandit
This paper presents a model to approach the dynamics of infectious diseases expansion. Our model aims to establish a link between traditional simulation of the Susceptible-Infectious (SI) model of disease expansion based on ordinary differential equations (ODE), and a very simple approach based on both connectivity between people and elementary binary rules that define the result of these contacts. The SI deterministic compartmental model has been analysed and successfully modelled by our method, in the case of 4-connected neighbourhood.
Epidemiological method of research, structure & Maintenance. Eneutron
Epidemiology is the study of disease patterns in populations and uses a systematic method of research to identify risk factors and determine preventive measures. This document discusses the epidemiological method, epidemiological diagnostics, and the system of epidemiological surveillance. The epidemiological method involves descriptive, analytical, and experimental techniques to study disease occurrence and justify prevention. Epidemiological diagnostics provides data to support preventive actions by describing disease manifestations, risk groups, determining causes, and formulating hypotheses. Epidemiological surveillance is the ongoing assessment of disease trends to enable timely intervention through prevention and control programs.
The SIR Model and the 2014 Ebola Virus Disease Outbreak in Guinea, Liberia an...CSCJournals
This document presents a mathematical model using the SIR (Susceptible, Infected, Recovered) model to understand the spread of the 2014 Ebola virus disease outbreak in Guinea, Liberia, and Sierra Leone. The model divides the population into compartments based on disease status. Differential equations are formulated and numerically solved using data from the outbreak. The results show that initially the number of infected individuals increases, reaches a peak, and then decreases as individuals recover or die, indicating the outbreak could be controlled. Public health interventions that reduce transmission rates can help an outbreak die out by lowering the reproduction number below 1.
This document presents a mathematical model analyzing the global stability of the endemic equilibrium of HIV/AIDS with a drug-resistant compartment. The model divides the total population into five compartments: susceptible individuals, infectious individuals not receiving treatment, infectious individuals receiving treatment who are drug resistant, infectious individuals receiving treatment who are not drug resistant, and AIDS individuals. The basic reproduction number is calculated using next generation matrix methods. Analysis using Lasalle's invariant principle finds that the endemic equilibrium of the model is globally asymptotically stable.
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2024 HIPAA Compliance Training Guide to the Compliance OfficersConference Panel
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Agent-based Models in malaria elimination strategy design
J. Ferrer1*
, J. Albuquerque2
, C. Prats1
, D. López1
and J. Valls1
11
Escola Superior d'Agricultura de Barcelona, Departament de Física i Enginyeria Nuclear.
Universitat Politècnica de Catalunya. C/ Esteve Terradas, 8. E-08860 Castelldefels
(Barcelona), Spain.
1
Department de Física i Enginyeria Nuclear, Escola Superior d´Agricultura de Barcelona, Universitat Politècnica de Catalunya, Castelldefels,
Spain. jordi.ferrer-savall@upc.edu, clara.prats@upc.edu, daniel.lopez-codina@upc.edu quim.valls@upc.edu. +34.93.552.11.28.
2 Departamento de Estatística e Informática – Universidade Federal Rural de Pernambuco. Recife, PE, Brasil., joa@deinfo.ufrpe.br, ,
+55.81.3320.6491.
Abstract: The present work evaluates the methodology to plan, communicate and discuss specific interventions to tackle malaria
spreading by comparing three representative and deliberately simple epidemic models: A) an epidemic continuous model of the human
population, B) a population-based model that accounts for both hosts and vectors, and C) an Individual-based model that considers the
same scenario as in (B). The paper proposes a standard protocol and the use of open-source and user-friendly simulation environments to
communicate and discuss the models.
Keywords: Agent-based models, Malaria, Epidemiology, Computational Modeling
Acknowledgement: We thank the financial support of the Ministerio de Ciencia y Tecnología (CGL 2007-65142)
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1. BACKGROUND
1. Strategies for malaria control, elimination and eradication
Malaria is a preventable and treatable disease that kills more than one million people per
year, most of them children in the poorest countries of the equatorial and tropical biomes.
Despite the efforts carried out to fight malaria, the burden of the disease is still severe in those
regions where the disease is endemic (WHO, 2008a).
Today, malaria eradication is back on the agenda (WHO, 2008b). Global programs against
malaria tackle a complex reality and deal with socioeconomic, logistic and environmental
factors. From the scope of health care alone, the scheme to be followed must consider
medical coverage scale-up and improvement, increasing control of the disease, generalized
elimination and sustained surveillance (Molyneux et al, 2004, Greenwood, 2009). Global
strategies regard temporal scales of the order of the decades and they contemplate
unforeseen events such as the advent of new parasite resistance to drugs or the breakdown
of local health systems (WHO, 2005). Yet, global strategies finally lie on local specific
interventions, carried out by agents with a limited scope of action.
Elimination in areas of high, stable malaria transmission and with unrelenting vector
prevalence requires new tools and the combination of interventions in multiple fronts (Boni,
2008; White et al, 2009). It also requires complicity and coordination of local agents and
communities. The economic and human cost to carry out each intervention is substantial.
Therefore, strategies at a local level must be carefully planned, easily communicated and
continually evaluated (Rosensweig, 2009).
2. Epidemiology measurements and actions to tackle malaria
Planning strategies finally reposes on experience, heuristics and keen insight. However,
several tools may serve to support and guide decision making.
Impact of malaria is usually assessed through parasite prevalence (PR) and the fraction of
clinical infections (p) (Smith and Hay, 2009). Predictions on malaria spreading are usually
based on a single population-based calculated parameter: the reproduction number (R0)
(Bailey, 1982). Interventions to target malaria aim to reduce R0 through the control of the
vector population (mosquitoes), the reduction of human exposition to bites, and the treatment
of symptomatic patients. Table 1 lists the usual measurements of malaria epidemics and the
potential interventions to control the disease (White et al, 2009).
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Basic measurements in malaria epidemiology
(i) Parasite prevalence (PR), (v) Number and location of sick people
(ii) Fraction of clinical infections (p) (vi) Location and density of vector populations
(iii) Fraction of infected mosquito vectors (vii) Entomological Inoculation Rate (EIR)
(iv) Average duration of host immunity while not
exposed to the parasite
(viii) Infective persistence of the parasite in blood
(ix) Average parasite load in blood
Interventions for malaria control and elimination
(1) RTS: vaccination or profilaxis. Induced reduction of the susceptibility to infection of hosts.
(2) MAST: mass-screen and treatment of host population. Reduction of the infected population that shows clinical symptoms
or detectable parasite load.
(3) TBDH: transmission-blocking drug mass treatment. Target: hepatocytes, to prevent vector > human transmission
(4) TBDG: transmission-blocking drug mass treatment. Target: gametocytes, to prevent human > vector transmission
(5) ITN: insecticide-treated bed nets. Reduction of host-vector transmissions.
(6) IVM: integrated vector management. Control and elimination of mosquito population.
Table 1: Current measurements of malaria impact and field actions against the spreading of the infection.
It must be pointed out that the coverage, continuity and monitoring of the interventions is
determinant to the effectiveness of any attempt to control and eliminate malaria.
3. Models: an indispensable tool to design and evaluate strategies
Models are abstract constructions by which we represent reality. Mathematical modeling
and computer simulations are indispensable tools in science. They allow the quantitative
analysis of problems to perform testable predictions with a defined degree of confidence.
They are also communication tools that provide a common scaffold to understand reality,
interpret observations and design strategies (Knols, 2010).
Population-based Models (PbMs) are top-down approaches that describe the dynamics of
populations as a whole, usually by means of differential equations. They are well established
in malaria fight (Bailey, 1982; Koella, 1991) and they are used to plan and evaluate global
long-term strategies for malaria control (Maude et al, 2009). They may include age structure
(Aguas et al, 2007) and heterogeneity (Lloyd, 2007) of the population, as well as stochasticity
in the modeled rules. This has the cost of increasing the model complexity and making it less
clear than an equivalent simpler model (White et al, 2009).
Individual-based Models (IbMs) (a.k.a. Agent-based Models, in social sciences) are
bottom-up approaches to complex systems, complementary to PbMs, that describe the
behavior of individual entities and which compute the evolution of the population from the
individual interactions with each other and with their local environment. (Grimm et al, 2005).
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The main role of IbM in malaria is to provide a better understanding of the host-vector system
(Mckenzie et al, 1998; Keeling and Grenfell, 2000; Gu et al, 2003).
IbMs are mechanistically rich: they offer an explicit connection between the parameters and
structure of the model and the mechanisms operating in reality (Ferrer et al, 2008). In
consequence, they are a clear and intuitive representation of the reality, as it is experienced
by the local agents (i.e. policy managers, health care workers and affected communities).
The purpose of this paper is to illustrate the suitability of the IbM approach as a prediction
and communication tool in the design implementation and evaluation of field interventions at a
local and short-range scales, complementary to the established PbM approach. It also
proposes the use of standard forms to communicate and analize models.
2. METHODS
We present three deliberately simple epidemiological models of stable falciparum malaria in
endemic region, we briefly review their structure and compare them one with each other and
with experimental data. These models are too naive to capture important features of the
disease, such as seasonality, multiplicity and coexistence of Plasmodium species and strains
or age structure in the host population, yet, they can provide deeper insight regarding
methodological issues. For this reason, their calibration is qualitative, and their comparison to
reality is approximate.
Two of the models are PbMs (Models A and B), and the third one is an IbM with the same
scheme as model B (Model C). Extended descriptions of each model and simulator are
presented in the Appendix (see Additional file). The three models have been implemented
and solved in the simulator platform NetLogo 4.1 (Wilensky, 1999).
2.1 Presentation of the models
Model A - SI1RI2: PbM that represents a constant (with no birth/death rates) host population
divided into four segments corresponding to four infection states: naïve susceptible (S),
infected showing clinical symptoms (I1), infected and asymptomatic (I2), and recovered and
partially immunized but susceptible (R). The fraction of population in each state varies
according to a set of transition rates defined in order to best fit the experimental
measurements, as shown in Figure 1. This model is inspired on two recognized PbMs (Aguas
et al, 2008; White et al, 2009).
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Model B - H(SI1RI2)-M(SI): PbM with the same structure as Model A for the host population
(S, I1, I2 and R) but which also considers a mosquito population divided into two segments:
susceptible (MS) and infectious (MI) vectors (Figure 1). It uses the same set of transition rates
as Model A but includes two new parameters for transitions between vector subpopulations.
Model C: Spatially explicit IbM operating with the scheme of Model B applied to each
individual (Figure 1). Its parameters are defined at an individual level and can be related to
field measurements.
A B
+
S I1
I2
R
MS
MI
1
1
τ
pM
rM
C
2
1
τ
A B
+
S I1
I2
R
MS
MI
1
1
τ
pM
rM
C
2
1
τ
Figure 1: Schemes of the three models. Model A: PbM SI1RI1, Model B: PbM H(SI1RI2)-M(SI). Model C: IbM H(SI1RI2)-M(SI). Human
figures represent subpopulations in A,B and individuals in C, in the infection states: susceptible -green, S-, clinically infected -red, I1--
asymptomatic infected -orange, I2- and recovered -grey, R-. Mosquitoes represent infectious and susceptible vector populations in Models A,
B and individuals in Model C. Black –healthy mosquito MS -, and white – infected carrier MI- . force of infection, irecovery time of
infection stage i, rate of loss of acquired immunity, pM: probability of infection of vector, rM: renewal rate of vector.
The direct outputs of the models are parasite prevalence (PR) and fraction of clinical
infections (p). They are obtained at each time step (t) with:
(t)
I
+
(t)
I
+
R(t)
+
S(t)
(t)
I
+
(t)
I
=
(t)
PR
2
1
2
1
[1]
(t)
I
(t)
I
=
(t)
p
2
1
. [2]
Model C has two additional output variables, the force of infection, , and the Entomological
Inoculation Rate, (EIR, Kelly-Hope and McKenzie, 2009) which can be compared with field
measurements and with the input parameters of the PbMs.
ness
contagious
of
duration
x
hosts
e
susceptibl
of
Number
contagions
host
of
Number
=
λ(t) [3]
hosts
of
Number
contagions
host
of
Number
=
EIR(t) . [4]
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2.2 Relation between the parameters of the models and the real measurements
Parameter and biological meaning Models
Real-world
observation
* Real-world action
*
force of infection A (i)-(viii) (1)-(6)
irecovery rate of Ii A,B,C (viii) (2)
relative infectivity of 1 to 2 A,B,C (viii) (1)
loss of immunity A,B,C (iv) -
pH: infection probability of host B,C (vii) (1,3,5)
pM: infection probability of vector B,C (vi) ( 4, 5)
rM: death rate of vectors B,C (iii) (6)
Table 2: Parameters of equivalent PbM and IbM models and their relation with the real-world measurements and field interventions (* see
Table 1).
Table 2 presents the model parameters and relates them with real interventions and
measurements. Their values are inferred from data in the literature, taken from average
measurements or adjusted in such a way that model outcomes best fit the field observations
(Aguas et al, 2008). Table 2 shows that Model A is highly aggregated (Ferrer et al, 2009): it
incorporates a lot of the measurable information into a single parameter, the force of infection
( ). This makes it quite obscure, as alone mainly drives the evolution of the infection.
Moreover, this parameter is defined rather circularly:
2
1
2
0
I
+
R
+
I
+
S
α
I
+
I
R
=
λ 1
, [5]
where
2
1
0
1
τ
p
+
τ
p
β·
=
R , [6]
and
2
φI
+
I
λ
=
β
1
. [7]
Actually, this poses no problem from the practical point of view, as its value is set to best fit
the field data. Nevertheless, it makes a blind hodgepodge parameter, barely usable for the
design of particular strategies.
Model B replaces the parameter by the infection probability of hosts (pH), and adds two
parameters for the vector population: infection probability of (pM) and renewal rate (rM).
Shifting from Model B to Model A only requires calculating as:
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M
M
H
r
·p
p
=
λ [8]
Model C uses the same infection scheme as Model B but its parameters are individually
defined. For instance, pH stands for the probability of a host being infected when coming upon
a carrier vector, rather than the rate of vectors infecting hosts. Thus, they are more easily
linked one-to-one with real-world phenomena, which may be independently modified by
different field interventions.
3 RESULTS AND DISCUSSION
3.1 Comparison of the outcome of the three models
Models A, B and C have been compared one to each other and to experimental data
available in the literature (Aguas et al, 2008; White et al, 2009). As a result of this
comparison, it is found that:
1. the three models show similar temporal evolutions for compatible sets of parameters
(Figure 2),
C
B
A C
B
A
Figure 2: Temporal evolutions of the three models after a one-time intervention that reduced parasite prevalence. Each line shows the
percentage of the segments of host population through time.
2. the outcome (long-term parasite prevalence, PR, and reproduction number, R0) of all
three models can be fitted to different sets of regional experimental data (Figure 3).
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a) b)
Bakau
Foni Kansala
Kilifi
a) b)
Bakau
Foni Kansala
Kilifi
Figure 3: Parasite prevalence as a function of the force of infection . a) Experimental and simulated data, reprinted from Aguas et al,
2008, PlosOne 3 (3) e17676, showing parasite prevalence for different regions characterized by different forces of infection. b) Simulation
outcomes obtained with models A, B and C.
These results show that the three models are alike, in accordance with what previous
results indicated (White et al, 2009). Nevertheless, they have some intrinsic differences,
which are outlined below.
First, PbMs and IbMs cope with different sets of field measurements and perform optimally
at different levels of description. While IbMs can include detail for specific measurements and
interventions, PbMs are better to deal with average magnitudes that do not account for such
particularities. In particular, results obtained from an IbM may be used to define the input
parameters of a PbM.
Second, PbMs are easily handled with mathematical analysis. Their interpretation is
straightforward and only requires mathematical skills, while IbMs require the use of computers
and performing a statistical number of simulations to obtain significant outcomes. Recent
efforts have been addressed to build a standard formalism for analyzing and communicating
IbMs (Grimm et al, 2006; Grimm et al, 2010).
Third, PbMs use real numbers instead of the whole numbers used by IbMs. This allows
dealing with great populations, but presents problems for smaller ones due to the original
discrete essence of humans and mosquitoes. For instance, below the threshold of disease
elimination (R0 <1), PbMs asymptotically tend towards the disease elimination (PR→0) but,
unlike IbMs, never reach it. This is usually solved by considering that the disease has been
eliminated when PR reaches a prefixed negligible value, but poses a problem when dealing
with small populations or segments of the population (e.g. when a single carrier can import
the infection into a naive population).
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Finally, simple PbMs are often deterministic but IbMs usually consider stochasticity. Thus, a
PbM always gives the same outcome for a fixed set of parameters. An indirect consequence
of this is that qualitative changes in the outcome of a PbM may occur in very sharp
transitions. For instance, in the threshold R0 ≈ 1, if the model concludes that a certain
intervention must be maintained for a span teff in order to lead to elimination, it is actually
stating that any intervention lasting t = teff -is ineffective, while it works well as soon as t =
teff + , even for minute values of (Aguas et al, 2008). Such non-realistic behavior of
deterministic PbMs arises from the fact that continuous variables may present mathematical
singularities near some particular values. A similar phenomenon occurs due to the chaotic
behavior of the deterministic equations for certain sets of parameters. In this case, the
outcome is highly sensitive to initial conditions, rendering long-term prediction impossible. In
contrast, the dynamics of IbMs usually evolve towards fixed or limited attractors, this meaning
that their predictions are circumscribed around an average outcome, and show a definite
variance.
3.2 Results obtained with the IbM
Over 10,000 simulations have been carried out to scan the parameter space of Model C in a
closed system, in the short and middle-term (each run representing 1 year). Some general
results are listed below.
Stochastic variability of the model outcome is PR < 5 % after modifying only the
random seed.
Modifications on the density of host and/or vector populations strongly influence the
simulator outcome.
Several modeled actions may lead to local disease elimination:
Reducing the transmission rates pH and pM. These represent RTS and TBDH, and
TBDG strategies, respectively, in the real world. In order to reduce pM, it is important
to block the transmission from both the clinically infected and asymptomatic hosts.
Permanently reducing the population density of vectors, so that the host-vector
encounter is less probable. This represents IVM interventions.
The number of infective encounters is also reduced with ITN, which can be
represented in the model by the joint reduction of pM and pH.
Reducing the population of infected hosts, either through one-time interventions
that directly affect the percentage of I1 and I2 segments, or by reducing the duration
of contagiousness, 1 and 2, permanently or during lasting interventions.
Elimination strategies usually entail tackling both people with clinical malaria and
with an asymptomatic infection (Figure 4a). These modifications represent MAST
interventions in the real systems.
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Control IVM 50% ITN 50%
PR = 37%
= 0.51
EIR = 0.023
PR = 21.4%
= 0.23
EIR = 0.007
PR = 8.7%
= 0.11
EIR = 0.003
Model
output:
Model Scenario
Host
population
b)
Prevalence
1 (days)
2
(days)
_1
100
1000
100
10
a)
Control IVM 50% ITN 50%
PR = 37%
= 0.51
EIR = 0.023
PR = 21.4%
= 0.23
EIR = 0.007
PR = 8.7%
= 0.11
EIR = 0.003
Model
output:
Model Scenario
Host
population
b)
Prevalence
1 (days)
2
(days)
_1
100
1000
100
10
a)
Figure 4: Simulation results of Model C. a) Response of the IbM to variation of the duration of clinical infection (1) and asymptomatic
infection (2). The model shows that elimination can be achieved by reducing 1 and 2. b) Comparison between the effect of reducing vector
density (IVM) and mosquito bites (ITN) by 50% on a given scenario. PR: parasite prevalence, l: force of infection and EIR: Entomological
Inoculation Rate.
This naive IbM allows comparing two different interventions on the same scenario, which
may be useful to evaluate cost efficiency of alternative options. For instance, comparing
MAST addressed either to asymptomatic or to clinically infected people (Figure 4a), or
comparing a reduction of 50% vector population with IVM against protecting half of the people
with ITN (Figure 4b).
4. CONCLUSIONS AND FURTHER WORK
Models are an essential tool to help decision making and to evaluate specific strategies.
Simple mathematical models based on a population approach are a good tool to deal with the
long-term (i.e. covering several years or even decades) evolution of the disease at a regional
scale but find some limitations to capture features of smaller communities and shorter time
scales.
IbMs are not necessarily more complicated than population models based on continuous
equations. Their parameters have a more direct connection with field measurements and/or
specific actions and campaigns. This also entails that they are quite data-hungry, this makes
them less appropriate to study systems where the specific measurements are hardly
obtained.
IbMs are versatile. They can include heterogeneity among the population (e.g. in their
access and response to treatments), and can easily incorporate external fluxes (e.g. imported
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malaria), discrete interventions and unexpected events (e.g. abrupt cessation of interventions)
with slight modification of the model structure. For this reason, they represent a good tool for
the planning and evaluation of realistic field strategies in short temporal scales.
NetLogo and other user-friendly IbM modeling environments are appropriate frameworks to
use IbMs as communication tools, to represent and discuss local strategies against malaria
with decisive agents (MOSIMBIO, 2010). They are adequate for non-modelers and policy
makers because they present very simple interfaces that allow an easy manipulation of the
real-world schemes.
The models presented here are deliberately simple in nature. They are put forward to
illustrate essential differences in the methodological approaches to model malaria epidemics,
rather than to present realistic results on specific systems. Nevertheless, even such simple
models provide important insight. The most remarkable result in this sense is that
asymptomatic infected people represent a permanent pool of the parasite that must be
tackled to achieve elimination.
Perspectives for the current model include its application to the analysis of past and future
strategies for controlled real-world communities, for instance, IBMs can include Geographic
Information Systems (GIS) in spatially explicit frameworks and they can account for
particularities of specific scenarios (Perez and Dragicevic, 2009). The IbM is currently in the
process of being adapted to allow for the description of the epidemiology in the national park
of Jaú, Brasil. This requires an increase of complexity of the model to incorporate
geoepidemiological constraints of the rainforest biomes, ecological landscapes of multiple
parasite and strains, and specific traits of P. vivax. Nevertheless, these improvements do not
affect the scaffolding of the model and can be easily integrated in the IbM structure.
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AUTHOR INFORMATION AND CONTRIBUTIONS
DL and JV are senior researchers in IbM of complex systems in the context of theoretical ecology and applied microbiology. JF and CP are
two postdocs from the same group, MOSIMBIO. JA is a Professor at the Department of Statistics and Informatics of the Federal Rural
University of Pernambuco, Brazil, where he coordinates the Research Group for Computational Modeling..
JF designed the models and carried out the simulations. CP and DL proposed the virtual experiments and criteria to compare the models
with each other and with the experimental results. CP and JV assisted in the calibration, analysis of sensitivity and optimization of the
simulator. JF and JA shaped the discourse of the manuscript