Modelling the structure of dependence of stock markets in BRICS & KENYA: Copula GARCH approach

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc.SS) at Strathmore UniversityBackground: Dependence structure is used widely to describe relationships between risks and provides estimation of risks for risk management purposes. Modeling dependence structure of stock returns is a difficult task when returns are having non elliptical distributions. Objective: To examine the dependence pattern between the Kenya stock market return and BRICS stock market returns. Methods: In this dissertation, we estimated the dependence using copula GARCH, an approach that combines copula functions and GARCH models. We applied this method to a stock market returns consisting of stock indices of Brazil, Russia, India, China and South Africa (BRICS) and Kenya stock market. We first used GARCH(1,1) to model the marginal distributions of each stock returns using different GARCH(1,1) specifications. Copula was then used to analyze the dependence between the BRICS stock market returns and Kenya stock market returns using the standardized marginal distributions derived from GARCH(1,1) residuals. The best fitting copula parameter was determined using the log likelihood or AIC.Results: Empirical results showed that GJR-GARCH model provided the best fit for Brazil, Russia, China and Kenya while E-GARCH model provided the best fit for India and South Africa. As for modeling the dependence structure, student t copula parameter provided the best fit for the marginal distributions of the returns. Conclusion: Marginal models showed presence of volatility clustering which vanishes after crisis. To capture the dependence structure for bi variate data sets, Student t copula was considered to be the appropriate copula function. Recommendation: Further research should be extended to examine the multivariate structure, a joint distribution of BRICS in terms of Multivariate GARCH. Also research should focus on specific time periods in order to ensure effectiveness in measurement and management of risks

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Malaria diagnosis in rural healthcare facilities and treatment-seeking behavior in malaria endemic settings in western Kenya

Accurate malaria diagnosis and timely treatment are requirements for effective management of the disease. However, treatment efficacy may be significantly reduced in resource-constrained healthcare facilities with poorly equipped laboratories and frequent drug and rapid diagnostic test kit (RDT) stock-outs. Furthermore, patient may avoid seeking treatment from such facilities. The study's goal was to determine treatment-seeking behavior, malaria diagnosis and treatment quality, and likely treatment-seeking determinants in the local population. Passive case detection, which targeted all patients with suspected malaria cases, was conducted in ten public healthcare facilities over a three-month period. Monthly malaria cases, methods of diagnosis and antimalarial drug availability were assessed. A household-based survey was also carried out. Structured questionnaires were used to collect knowledge, attitude and practice (KAP) data from household heads. Malaria knowledge, treatment seeking behavior, and predictors of malaria treatment-seeking were all determined. Three of the seven dispensaries lacked a laboratory to conduct microscopy- diagnosis. These three dispensaries also experienced frequent RDT stock-outs, which resulted in depending on clinical signs as diagnosis for malaria. The majority of local residents with fever (50.3%) purchased antimalarial drugs from a chemist. About 37% of fever patients sought treatment at healthcare facility while the remaining 12.7% did not treat their fevers. In irrigated areas, 45.5% (46/64) of fever patients sought treatment at healthcare facilities, compared to 25% (18/64) in non-irrigated areas (p = 0.009). Most children aged below 5 who had fever (77.7%) were taken to healthcare facility for treatment compared to 31.4% of children aged 5-14 years or 20.9% of adults (0.0001). Predictors of treatment seeking included access to healthcare facility (OR = 16.23, 95% CI: 2.74-96.12), and ability to pay hospital bills (OR = 10.6, 95% CI: 1.97-57). Other factors that influenced health-seeking behavior included the severity of symptoms, the age of the patient and knowledge of malaria symptoms

Impact of Agricultural Irrigation on Anemia in Western Kenya.

Expanding agricultural irrigation efforts to enhance food security and socioeconomic development in sub-Saharan Africa may affect malaria transmission and socioeconomic variables that increase the risk of anemia in local communities. We compared the prevalence of anemia, Plasmodium falciparum infection, and indicators of socioeconomic status related to nutrition in communities in Homa Bay County, Kenya, where an agricultural irrigation scheme has been implemented, to that in nearby communities where there is no agricultural irrigation. Cross-sectional surveys conducted showed that anemia prevalence defined by WHO criteria (hemoglobin < 11 g/dL) was less in communities in the irrigated areas than in the non-irrigated areas during the wet season (38.9% and 51.5%, χ2 = 4.29, P = 0.001) and the dry season (25.2% and 34.1%, χ2 = 7.33, P = 0.007). In contrast, Plasmodium falciparum infection prevalence was greater during the wet season in irrigated areas than in non-irrigated areas (15.3% versus 7.8%, χ2 = 8.7, P = 0.003). There was, however, no difference during the dry season (infection prevalence, < 1.8%). Indicators of nutritional status pertinent to anemia pathogenesis such as weekly consumption of non-heme- and heme-containing foods and household income were greater in communities located within the irrigation scheme versus those outside the irrigation scheme (P < 0.0001). These data indicate that current agricultural irrigation schemes in malaria-endemic communities in this area have reduced the risk of anemia. Future studies should include diagnostic tests of iron deficiency, parasitic worm infections, and genetic hemoglobin disorders to inform public health interventions aimed at reducing community anemia burden

Monthly suspected and confirmed malaria cases from the 10 selected health facilities within the study area.

Monthly suspected and confirmed malaria cases from the 10 selected health facilities within the study area.</p

Malaria knowledge, attitude, and practice (KAP) survey.

Malaria knowledge, attitude, and practice (KAP) survey.</p

Malaria treatment seeking behavior survey.

Accurate malaria diagnosis and timely treatment are requirements for effective management of the disease. However, treatment efficacy may be significantly reduced in resource-constrained healthcare facilities with poorly equipped laboratories and frequent drug and rapid diagnostic test kit (RDT) stock-outs. Furthermore, patient may avoid seeking treatment from such facilities. The study’s goal was to determine treatment-seeking behavior, malaria diagnosis and treatment quality, and likely treatment-seeking determinants in the local population. Passive case detection, which targeted all patients with suspected malaria cases, was conducted in ten public healthcare facilities over a three-month period. Monthly malaria cases, methods of diagnosis and antimalarial drug availability were assessed. A household-based survey was also carried out. Structured questionnaires were used to collect knowledge, attitude and practice (KAP) data from household heads. Malaria knowledge, treatment seeking behavior, and predictors of malaria treatment-seeking were all determined. Three of the seven dispensaries lacked a laboratory to conduct microscopy- diagnosis. These three dispensaries also experienced frequent RDT stock-outs, which resulted in depending on clinical signs as diagnosis for malaria. The majority of local residents with fever (50.3%) purchased antimalarial drugs from a chemist. About 37% of fever patients sought treatment at healthcare facility while the remaining 12.7% did not treat their fevers. In irrigated areas, 45.5% (46/64) of fever patients sought treatment at healthcare facilities, compared to 25% (18/64) in non-irrigated areas (p = 0.009). Most children aged below 5 who had fever (77.7%) were taken to healthcare facility for treatment compared to 31.4% of children aged 5–14 years or 20.9% of adults (0.0001). Predictors of treatment seeking included access to healthcare facility (OR = 16.23, 95% CI: 2.74–96.12), and ability to pay hospital bills (OR = 10.6, 95% CI: 1.97–57). Other factors that influenced health-seeking behavior included the severity of symptoms, the age of the patient and knowledge of malaria symptoms.</div

Demographic information of the households visited.

Demographic information of the households visited.</p

List of the 10 healthcare facilities within the study area.

List of the 10 healthcare facilities within the study area.</p

Knowledge, attitude, and practice towards malaria.

Knowledge, attitude, and practice towards malaria.</p