33 research outputs found
Operationalisation of Model for Dynamics of COVID-19 in Kenya: Trajectory of Omicron Wave in Kenya
No full textJournal articleKenya has experienced five COVID-19 surges driven by Alpha, Beta, Delta
(2x), and Omicron. These waves are accurately predicted by the OTOI-NARIMA
model. Consequently, in Kenyan Lake Region Economic Bloc (LREB), private
sector and NGO partnerships have been forged to strengthen regional health
systems and prepare effectively for epidemic resurgence. The co-development
and implementation of the so-called “LREB COVID-Dx” digital platform
enable efficient epidemic monitoring in semi-real time, referral of patients,
optimal use of limited resources, and community of practice among regional
health practitioners. In this paper, we describe the practical implementation
of the OTOI-NARIMA model and COVID-Dx digitized platform in Kenyan
COVID-19 reality, with emphasis on the latest Omicron wave. In estimating
the trajectory of Omicron wave, 612 data points of daily case infections are
used. The order of moving average is calculated and corresponds to reproduction number, R0. The series are normalized, superimposed, and used to derive OTOI-NARIMA model. The model is estimated and interpreted. Test statistics including Ljung-Box test, ACF, and PACF are conducted. The COVID-Dx data digitization is used to inform epidemic preparedness. The OTOI-NARIMA model in general successfully established the periodicity and seasonality of COVID-19 resurgences in Kenya. The model is used to inform preparedness, including vaccines rollout. During alert stages of the wave, on December 4, 2021, the model was reused to nowcast the trajectory of the wave. Omicron wave was projected to peak in Kenya between November 23, 2021, and January 4, 2022. The wave showed strong likelihood of declining after January 29, 2022. In reality, Omicron wave was experienced from November 27, 2021, to January 29, 2022. The model predicted that Omicron variant will have run its full course by June 22, 2022, and possibly replaced by another variant, recombinant or sub-variant. According to OTOI-NARIMA model, dominant variants are replaced after every six months, which gives insights into suitable periods for administration of vaccine boosters. The total number of Kenyan patients (symptomatic or asymptomatic) during Omicron resurgence was estimated to be ~4.5 million. The total number of patients hospitalized during the wave is estimated to be ~2000. Effective, efficient, and economical response to Omicron resurgence in LREB benefitted from meticulous infusion of mathematical modelling and digitization of relevant data for epidemic preparedness and rapid decision making. The study has two limitations: Incomplete merging of stochastic processes and deterministic methods; calculating with accuracy the period it takes to fully replace a dominant COVID-19 variant. These two limitations may be considered for further research
The Overlooked Crisis of Post-Covid Kidney Disease
No full textnewspaperGlobally, by the year 2021, around 144.7 million people (3.7 per cent) had developed long Covid, and that estimate has since double
Defeating COVID-19: In The African Frontline
No full textbooksThe story of unlikely success in Africa's battle against Covid-19 pandemic, told from the front lines. A story of local experts, local leadership and local resources. a reason why Africa's new public Health order is promising. One Health and Digital Health must be relooked and mainstreamed.
It's a story of what to do when anybody cannot help you
Exploring the Statistical Significance of Africa's COVID-19 Data
No full textJournal articleIt has been cited by different researchers that COVID-19 infections in Africa is insignificant. This paper delves into the regional data to scrutinize the statistical significance of COVID-19 in Africa. The data of all regions, according to World Health Organization (WHO) classification, is compared to that of Africa. The paper explores COVID-19 infections data including cases, fatality, case fatality rates, recovery, and recovery rates. These are compared to COVID-19 status in Africa on May 9, 2020. First, the COVID-19 regional data is taken through logarithmic transformation, normality tests and One-way ANOVA analysis of mean infections, fatality, case fatality rates, recoveries, and case recovery rates. Then Tukey post hoc method is used to identify which regions exhibit statistical difference in, cases, fatality, case fatality rates, recoveries, and case recovery rates. Estimation of linear models of various parameters with regions as factor is done. The residuals of the linear models are tested for normality using Q-Qplots, residual-fitted plots, and histograms. Lastly, 95% family-wise confidence level of regional mean differences in COVIFD-19 infections and resultant effects is estimated and plotted. In this paper selected countries in the East, West, and mid- west Mediterranean, and Oceania regions are referred to as OCEA. In the statistical analysis the regions are denoted as Americas (AMER), Europe (EURO), Africa (AFRO), and OCEA. Results indicate that the mean COVID-19 infection cases are significantly different from Americas, Europe, and OCEA at 95% confidence level. Also, the mean COVID-19 case fatality in Africa is significantly different from Europe and Americas but not OCEA. In addition, mean COVID-19 case fatality rate in Africa is not statistically different from Americas, Europe, and OCEA at 95% confidence level. Further, the mean COVID-19 recoveries in Africa is significantly different from Europe and OCEA but not Americas at 95% confidence level. Interestingly, all regional recovery rates are not significantly different from each other at 95% confidence level
A Review of Predominance of Gross National Income in Human Development Index during COVID-19 Crisis
No full textJournal ArticleThe study explores the role of gross national income (GNI) in the Human Development Index (HDI), a key measure of social progress tied to the Sustainable Development Goals. Using 2021 macroeconomic data from 170 countries, the analysis applies principal component analysis (PCA) and logistic regression to evaluate the relative contribution of HDI predictors: GNI, life expectancy (LE), and mean years of schooling (ME). Data underwent transformation and normalization to ensure accuracy. PCA results indicate that the first principal component (PC1), largely representing income, explains 41.8% of the total variance, with GNI having the strongest positive loading (0.660), while LE and ME load modestly and inversely. Logistic regression shows GNI declined 5.3 times, but ME and LE increased the likelihood of GNI growth by 2.2 and 4.7 times respectively, while population reduced it by 61%. The findings underscore GNI’s dominant role in HDI, urging global emphasis on income improvement to drive social progress
East Africa’s Health Systems Must Stay Vigilant as Excitement Builds around Chan
No full textnewspaper. As Chan football unites fans across borders, fragile disease surveillance systems face renewed Mpox and Ebola threats.
. East Africa’s celebrations mask rising risks, with Mpox deaths and porous borders fuelling transmission concerns
Relationship between Health Funding and Detection of Infectious Diseases
No full textJournal articleIn Kenya, the County Governments manage most health facilities that handle, store and transfer biological agents in response to potential health threats with limited information including biosecurity and biosafety. The County Government facilities include level 1, 2, 3, 4, 5 and the National Government manages level 6 facilities, national referral hos-pitals. The variables: infectious diseases; health development expenditure 2014/2015, and health current expenditure 2014/2015 indicate concern to achieve and maintain sustainable national health security. This study exam-ines the relationship between health funding and capacity of county level fa-cilities to report infectious human diseases between 2014 and 2015 in Kenya.
Results: The MLR model developed revealed that when annual develop-ment and recurrent health expenditure are held constant, the detection of new infection would remains at 78:017% (95% CI 78.4-
79.4); that 1% in-crease on health development expenditure increases detected infectious dis-eases by 23,180 cases per county; 1% increase in recurrent health expenditure increases detected infections by
286,639 cases per county.
Conclusion: Timely disbursement of funds to county governments could prevent emerging, re-emerging or deliberately exposed populations to viruses and other microbes that they otherwise would not have encountered. Fund-ing for budget activities on biosecurity and biosafety facilitates e ective compliance to biological threat reduction. Creating awareness among policy decision makers on critical health security funding gaps and marginalized communities to seek healthcare may achieve and sustain disease reporting rate by 80.01
Modelling Economic Determinants of Youth Unemployment in Kenya
No full textJournal articleYouth unemployment is a challenge to both developing and developed countries. The “youth bulge” and attending challenges of unemployment resulting in social evils and political violence (rioting, civil war and terrorism) are evident in Kenya. This study therefore analyzes the economic determinants of youth unemployment in Kenya using macroeconomic data from 1979 to 2012 by investigating empirical relationship among youth unemployment, gross domestic product, population, foreign direct investment, and external debt. The study used times series Autoregressive Distributed Lag model (ARDL) to test the long run effects of economic determinants of youth unemployment. At 5% significance level, empirical results indicate that unit increase in population by 1.1%; unit increase in foreign direct investment reduces youth unemployment by 0.00024%; unit increase in previous youth unemployment rate reduces current unemployment rate by 0.12%. Contrarily, 1% gross domestic product increases youth unemployment rate by 0.00559%. The study revealed that population growth; foreign direct investment, gross domestic product, and external debt have long run relationship with youth unemployment rate. This study therefore gives insights into possible solutions considering interplay of macroeconomic factors
Otoi-NARIMA Model for Forecast Seasonality of COVID-19 Waves: Case of Kenya
No full textJournal articleKenya has experienced three COVID-19 waves which left authorities mandated to do disease surveillance and estimate the burden of disease in a complex and uncertain environment with citizens’ trust in institutions wavering having lost jobs and incomes. The citizens’ vulnerability worsened with inability to connect to social support when each household wellbeing and financial ability came under threat causing much anxiety about the future. Mathematical modelling of the spread of disease ninforms surveillance, planning, budgeting, and response to save lives and livelihoods. In that regard, accuracy of predictions and forecasts is highly desirable. The length of duration of COVID-19 waves, the likely start and end dates, and the number of daily infections need to be estimated with precision. These inform and provide a window for authorities working in a holistic and integrated manner with researchers and experts to protect people especially the most vulnerable populations and communities to fully acquire WHO approved vaccines before the subsequent forecasted period of COVID-19 waves
Prediction and Forecasting Covid-19 Cases, Fatalities, and Morbidity in Kenya
No full textJournal ArticleIn this paper we are looking for the best model to predict COVID-19 cases, fatalities, and active cases. Time series analysis using auto-regressive integrated moving averages is used. The three series are tested for stationarity using Augmented Dickey-Fuller test (ADF) and differenced to obtain stationarity. Both the data and models formulated are tested for autocorrelation using ACF and PACF. In selecting the bestn ARIMA model Akaike Information Criteria (AIC) that gives the least value is picked. According to AIC selection the best model for cases, fatalities, and active/infected are ARIMA (2, 2, 2), (1, 2, 3), and (0, 2,1) respectively. It implies that two lags of previous cases have influence on current case occurrence as opposed to one lag of fatalities. Asymptomatic and infected people with clear syndromes, not in quarantine, may move and interact with others leading to more infections, compared to deceased. The residuals of estimated case fatalities and cases models are not autocorrelated as seen from the ACF and PACF. Also, the forecast of case fatality model show that fatalities will stabilize on 9/8/2020 and begin to fall from 22/8/2020.The cases model forecasts that COVID-19 cases stabilize after 17/8/2020 and begin to fall from 23/8/2020, both dates will have 35,779 and 41,517 COVID-19 total cases respectively. From further analysis, the forecast of active cases over time gives additional information; its lower limit forecast begins to fall after 10/8/2020 showing 13669 infected persons and reduces to 10345 on 4/9/2020. Consequently, 10/8/2020 is a possible beginning of Kenyan peak. There is also a statistical possibility that the peaks of cases, fatalities, and infected persons occur at different intervals of time or rotating seasonal peaks. The progress of active cases over time carries the “energies” or “momentum” of COVID19 pandemic
