The epidemiology of tuberculosis in Kenya, a high TB/HIV burden country (2000-2013)

Interest in the epidemiology of TB was triggered by the re-emergence of tuberculosis in the early 1990’s with the advent of HIV and falling economic status of many people which subjected them to poverty. The dual lethal combination of HIV and poverty triggered an unprecedented TB epidemic. In this study, we focused on the period 2000-2013 and all the notified data in Kenya was included. Data on estimates of TB incidence, prevalence and mortality was extracted from the WHO global Tuberculosis database. Data was analysed to produce trends for each of the years and descriptive statistics were calculated. The results showed that there was an average decline of 5% over the last 8 years with the highest decline being reported in the year 2012/13. TB continues to disproportionately affect the male gender with 58% being male and 42% being female. Kenya has made significant efforts to address the burden of HIV among TB patients with cotrimoxazole preventive therapy (CPT) uptake reaching 98% and ART at 74% by the end of 2013. Kenya’s TB epidemic has evolved over time and it has been characterised by a period where there was increase in the TB cases reaching a peak in the year 2007 after which there was a decline which began to accelerate in the year 2011. The gains in the decline of TB could be attributed in part to the outcomes of integrating TB and HIV services and these gains should be sustained. What is equally notable is the clear epidemiologic shift in age indicating reduced transmission in the younger age groups

Spatial temporal modelling of tuberculosis in Kenya using small area estimation

Tuberculosis, a highly infectious disease which is transmitted within and between communities when infected and susceptible individuals interact. Tuberculosis at present is a major public health problem and continues to take toll on the most productive members of the community. An understanding of disease spread dynamics of infectious diseases continues to play a critical role in design of disease control strategies. Modeling of Tuberculosis is useful in understanding disease dynamics as it will guide the importance of basic science as well as public policy, prevention and control of the emerging infectious disease and modeling the spread of the disease. This study sought to establish how long under different frameworks will TB disease recede to extinction. In this study, deterministic and stochastic models for the trends of tuberculosis cases over time in Kenya were developed. Susceptible Infective (SI), Susceptible Infective and Recovered (SIR) and Susceptible Exposed Infective and Recovered (SEIR) models were considered. These models were modified in order to fit the data more precisely (age structure and predisposing factors of the incident cases). The SIR and SEIR model with non-linear incidence rates were further looked at and the stability of their solutions were evaluated. The results indicate that both deterministic and stochastic models can give not only an insight but also an integral description of TB transmission dynamics. Both deterministic and stochastic models fit well to the Kenyan TB epidemic model however with varying time periods. The models show that for deterministic model the number of infected individuals increases dramatically within three years and begins to fall quickly when the transmissible acts are 10 and 15 and falls to close to zero by 15 years but when the transmissible act is 5 the number infected peaks by the 11th year and declines to zero by year 31, while for stochastic models the number infected falls exponentially but when the transmissible acts is 15 the decline is slow and will get to zero by the 53rd year while for 10 transmissible acts to declines to zero by the 18th year. The other transmissible acts (1, 3, 5) decline to zero by the 9th year. From this study we conclude that if the national control program continues with the current interventions it could take them up to the next 31 years to bring the infection numbers to zero if the deterministic model is considered, while in the stochastic model with accelerated interventions and high recovery rate and assuming that there is no change in the risk factors it could take them up to 11 years to bring the infections to zero

An application of deterministic and stochastic processes to model evolving epidemiology of tuberculosis in Kenya

Tuberculosis, a highly infectious disease which is transmitted within and between communities when infected and susceptible individuals interact. Tuberculosis at present is a major public health problem and continues to take toll on the most productive members of the community. An understanding of disease spread dynamics of infectious diseases continues to play a critical role in design of disease control strategies. Modeling of Tuberculosis is useful in understanding disease dynamics as it will guide the importance of basic science as well as public policy, prevention and control of the emerging infectious disease and modeling the spread of the disease. This study sought to establish how long under different frameworks will TB disease recede to extinction. In this study, deterministic and stochastic models for the trends of tuberculosis cases over time in Kenya were developed. Susceptible Infective (SI), Susceptible Infective and Recovered (SIR) and Susceptible Exposed Infective and Recovered (SEIR) models were considered. These models were modified in order to fit the data more precisely (age structure and predisposing factors of the incident cases). The SIR and SEIR model with non-linear incidence rates were further looked at and the stability of their solutions were evaluated. The results indicate that both deterministic and stochastic models can give not only an insight but also an integral description of TB transmission dynamics. Both deterministic and stochastic models fit well to the Kenyan TB epidemic model however with varying time periods. The models show that for deterministic model the number of infected individuals increases dramatically within three years and begins to fall quickly when the transmissible acts are 10 and 15 and falls to close to zero by 15 years but when the transmissible act is 5 the number infected peaks by the 11th year and declines to zero by year 31, while for stochastic models the number infected falls exponentially but when the transmissible acts is 15 the decline is slow and will get to zero by the 53rd year while for 10 transmissible acts to declines to zero by the 18th year. The other transmissible acts (1, 3, 5) decline to zero by the 9th year. From this study we conclude that if the national control program continues with the current interventions it could take them up to the next 31 years to bring the infection numbers to zero if the deterministic model is considered, while in the stochastic model with accelerated interventions and high recovery rate and assuming that there is no change in the risk factors it could take them up to 11 years to bring the infections to zero

Accuracy Assessment of the ESA CCI 20M Land Cover Map: Kenya, Gabon, Ivory Coast and South Africa

This working paper presents the overall and spatial accuracy assessment of the European Space Agency (ESA) 20 m prototype land cover map for Africa for four countries: Kenya, Gabon, Ivory Coast and South Africa. This accuracy assessment was undertaken as part of the ESA-funded CrowdVal project. The results varied from 44% (for South Africa) to 91% (for Gabon). In the case of Kenya (56% overall accuracy) and South Africa, these values are largely caused by the confusion between grassland and shrubland. However, if a weighted confusion matrix is used, which diminishes the importance of the confusion between grassland and shrubs, the overall accuracy for Kenya increases to 79% and for South Africa, 75%. The overall accuracy for Ivory Coast (47%) is a result of a highly fragmented land cover, which makes it a difficult country to map with remote sensing. The exception was Gabon with a high overall accuracy of 91%, but this can be explained by the high amount of tree cover across the country, which is a relatively easy class to map

An Epidemiological Profile of Malaria in Mali. A Report Prepared for the Ministry of Health, Mali, the Roll Back Malaria Partnership and the Department for International Development, UK. February, 2015.

No abstract available