Matching Items (6)

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An Evaluation of HEAL International's Health Education Program in Tanzania, Africa

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This study investigated the potential efficacy of HEAL International's prevention education program in inducing health behavior change in HIV/AIDS, malaria, and communicable disease to children in grade levels ranging from

This study investigated the potential efficacy of HEAL International's prevention education program in inducing health behavior change in HIV/AIDS, malaria, and communicable disease to children in grade levels ranging from primary school to secondary school. The health education program was aimed at changing health behavior by increasing knowledge. This increase in knowledge was analyzed as a modifying factor in the Health Belief Model suggesting that knowledge, along with five other modifying factors, are directly responsible for an individual's health perceptions. These health perceptions ultimately result in an individual's health behavior. As a result, it is argued that an increase in knowledge can lead to health behavior change so long as it is coupled with a strong theoretical framework. Administering pre-evaluations at the beginning of the program, post evaluations at the end of the program, and a second post evaluation again two months later completed the evaluation. It was hypothesized that if there was a significant difference between the percent of correct answers at the pre-evaluation compared the second post-evaluation then there is evidence that HEAL's health education program is, or at least has the potential to, create sustainable health behavior change. A paired samples t-test was completed on the data and showed a statistically significant difference between the percent of correct answers at pre-evaluation and the percent of correct answers at second post-evaluation. These results indicated that the number of students with a comprehensive knowledge of the subjects that HEAL taught during the program had increased. It was concluded that the results of the study indicate evidence that HEAL's program has the potential to deliver sustainable health behavior change but that it will be more quantifiable once HEAL is able to adopt a theoretical framework on which to base future programs.

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  • 2014-05

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Plasmodium Cost of Resistance and Life Stage Development within the Mosquito Vector

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Hundreds of thousands of people die annually from malaria; a protozoan of the genus Plasmodium is responsible for this mortality. The Plasmodium parasite undergoes several life stages within the mosquito

Hundreds of thousands of people die annually from malaria; a protozoan of the genus Plasmodium is responsible for this mortality. The Plasmodium parasite undergoes several life stages within the mosquito vector, the transition between which require passage across the lumen of the mosquito midgut. It has been observed that in about 15% of parasites that develop ookinetes in the mosquito abdomen, sporozoites never develop in the salivary glands, indicating that passage across the midgut lumen is a significant barrier in parasite development (Gamage-Mendis et al., 1993). We aim to investigate a possible correlation between passage through the midgut lumen and drug-resistance trends in Plasmodium falciparum parasites. This study contains a total of 1024 Anopheles mosquitoes: 187 Anopheles gambiae and 837 Anopheles funestus samples collected in high malaria transmission areas of Mozambique between March and June of 2016. Sanger sequencing will be used to determine the prevalence of known resistance alleles for anti-malarial drugs: chloroquine resistance transporter (pfcrt), multidrug resistance (pfmdr1) gene, dihydropteroate synthase (pfdhps) and dihydrofolate reductase (pfdhfr). We compare prevalence of resistance between abdomen and head/thorax in order to determine whether drug resistant parasites are disproportionately hindered during their passage through the midgut lumen. A statistically significant difference between resistance alleles in the two studied body sections supports the efficacy of new anti-malarial gene surveillance strategies in areas of high malaria transmission.

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  • 2021-05

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Analyzing Uncertainties Around Gene Drives: A Case Study of Mosquitos in Sub-Saharan Africa

Description

Malaria is a disease that has plagued human populations throughout history. Malaria is cause by the parasite Plasmodium, which uses mosquitoes as a vector for transfer. Current methods for controlling

Malaria is a disease that has plagued human populations throughout history. Malaria is cause by the parasite Plasmodium, which uses mosquitoes as a vector for transfer. Current methods for controlling malaria include issuing bed nets to citizens, spraying home with insecticides, and reactive medical care. However, using Clustered Regularly Interspaced Short Palindromic repeats (CRISPR) in conjunction with the Cas9 protein found in bacteria, the genomes of mosquitoes can be edited to remove the ability of mosquitoes to host Plasmodium or to create sex bias in which the birth rate of males is increased so as to make reproduction near impossible. Using CRISPR, this genome edit can be ‘driven’ through a population by increasing the likelihood of that gene being passed onto subsequent generations until the entire population possesses that gene; a gene drive can theoretically be used to eliminate malaria around the world. This paper identifies uncertainties concerning scientific, environmental, governance, economic ,and social aspects of researching and implementing gene drives and makes recommendations concerning these areas for the emerging technology of gene drives concerning the eradication of malaria using Sub-Saharan Africa as a case study

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  • 2017-05

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Plasmodium population structure in the context of malaria control and elimination

Description

Malaria is a vector-borne parasitic disease affecting tropical and subtropical regions. Regardless control efforts, malaria incidence is still incredible high with 219 million clinical cases and an estimated 660,000 related

Malaria is a vector-borne parasitic disease affecting tropical and subtropical regions. Regardless control efforts, malaria incidence is still incredible high with 219 million clinical cases and an estimated 660,000 related deaths (WHO, 2012). In this project, different population genetic approaches were explored to characterize parasite populations. The goal was to create a framework that considered temporal and spatial changes of Plasmodium populations in malaria surveillance. This is critical in a vector borne disease in areas of low transmission where there is not accurate information of when and where a patient was infected. In this study, fragment analysis data and single nucleotide polymorphism (SNPs) from South American samples were used to characterize Plasmodium population structure, patterns of migration and gene flow, and discuss approaches to differentiate reinfection vs. recrudescence cases in clinical trials. A Bayesian approach was also applied to analyze the Plasmodium population history by inferring genealogies using microsatellites data. Specifically, fluctuations in the parasite population and the age of different parasite lineages were evaluated through time in order to relate them with the malaria control plan in force. These studies are important to understand the turnover or persistence of "clones" circulating in a specific area through time and consider them in drug efficacy studies. Moreover, this methodology is useful for assessing changes in malaria transmission and for more efficiently manage resources to deploy control measures in locations that act as parasite "sources" for other regions. Overall, these results stress the importance of monitoring malaria demographic changes when assessing the success of elimination programs in areas of low transmission.

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Date Created
  • 2014

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Mathematics of climate change and mosquito-borne disease dynamics

Description

The role of climate change, as measured in terms of changes in the climatology of geophysical variables (such as temperature and rainfall), on the global distribution and burden of vector-borne

The role of climate change, as measured in terms of changes in the climatology of geophysical variables (such as temperature and rainfall), on the global distribution and burden of vector-borne diseases (VBDs) remains a subject of considerable debate. This dissertation attempts to contribute to this debate via the use of mathematical (compartmental) modeling and statistical data analysis. In particular, the objective is to find suitable values and/or ranges of the climate variables considered (typically temperature and rainfall) for maximum vector abundance and consequently, maximum transmission intensity of the disease(s) they cause.

Motivated by the fact that understanding the dynamics of disease vector is crucial to understanding the transmission and control of the VBDs they cause, a novel weather-driven deterministic model for the population biology of the mosquito is formulated and rigorously analyzed. Numerical simulations, using relevant weather and entomological data for Anopheles mosquito (the vector for malaria), show that maximum mosquito abundance occurs when temperature and rainfall values lie in the range [20-25]C and [105-115] mm, respectively.

The Anopheles mosquito ecology model is extended to incorporate human dynamics. The resulting weather-driven malaria transmission model, which includes many of the key aspects of malaria (such as disease transmission by asymptomatically-infectious humans, and enhanced malaria immunity due to repeated exposure), was rigorously analyzed. The model which also incorporates the effect of diurnal temperature range (DTR) on malaria transmission dynamics shows that increasing DTR shifts the peak temperature value for malaria transmission from 29C (when DTR is 0C) to about 25C (when DTR is 15C).

Finally, the malaria model is adapted and used to study the transmission dynamics of chikungunya, dengue and Zika, three diseases co-circulating in the Americas caused by the same vector (Aedes aegypti). The resulting model, which is fitted using data from Mexico, is used to assess a few hypotheses (such as those associated with the possible impact the newly-released dengue vaccine will have on Zika) and the impact of variability in climate variables on the dynamics of the three diseases. Suitable temperature and rainfall ranges for the maximum transmission intensity of the three diseases are obtained.

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Date Created
  • 2018

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Theoretical studies on a two strain model of drug resistance: understand, predict and control the emergence of drug resistance

Description

Infectious diseases are a leading cause of death worldwide. With the development of drugs, vaccines and antibiotics, it was believed that for the first time in human history diseases would

Infectious diseases are a leading cause of death worldwide. With the development of drugs, vaccines and antibiotics, it was believed that for the first time in human history diseases would no longer be a major cause of mortality. Newly emerging diseases, re-emerging diseases and the emergence of microorganisms resistant to existing treatment have forced us to re-evaluate our optimistic perspective. In this study, a simple mathematical framework for super-infection is considered in order to explore the transmission dynamics of drug-resistance. Through its theoretical analysis, we identify the conditions necessary for the coexistence between sensitive strains and drug-resistant strains. Farther, in order to investigate the effectiveness of control measures, the model is extended so as to include vaccination and treatment. The impact that these preventive and control measures may have on its disease dynamics is evaluated. Theoretical results being confirmed via numerical simulations. Our theoretical results on two-strain drug-resistance models are applied in the context of Malaria, antimalarial drugs, and the administration of a possible partially effective vaccine. The objective is to develop a monitoring epidemiological framework that help evaluate the impact of antimalarial drugs and partially-effective vaccine in reducing the disease burden at the population level. Optimal control theory is applied in the context of this framework in order to assess the impact of time dependent cost-effective treatment efforts. It is shown that cost-effective combinations of treatment efforts depend on the population size, cost of implementing treatment controls, and the parameters of the model. We use these results to identify optimal control strategies for several scenarios.

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Date Created
  • 2011