Parameter Estimation and Optimal Control of the Dynamics Of Transmission of Tuberculosis with Application to Cameroon

This paper deals with the problem of parameter estimation and optimal control of a tuberculosis (TB) model with seasonal fluctuations. We first present a uncontrolled TB model with seasonal fluctuations. We present the theoretical analysis of the uncontrolled TB model without seasonal fluctuations. After, we propose a numerical study to estimate the unknown parameters of the TB model with seasonal fluctuations according to demographic and epidemiological data from Cameroon. Simulation results are in good accordance with the seasonal variation of the new active reported cases of TB in Cameroon. Using this TB model with seasonality, the tuberculosis control is formulated and solved as an optimal control problem, indicating how control terms on the chemoprophylaxis and treatment should be introduced in the considered TB model to reduce the number of individuals with active TB. Results provide a framework for designing cost-effective strategies for TB with two strategies of intervention

Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings

In low-resource settings, prevalence mapping relies on empirical prevalence data from a finite, often spatially sparse, set of surveys of communities within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model with binomial error distribution, logistic link and a combination of explanatory variables and a Gaussian spatial stochastic process in the linear predictor. In this paper, we first review statistical methods and software associated with this standard model, then consider several methodological extensions whose development has been motivated by the requirements of specific applications. These include: methods for combining randomised survey data with data from non-randomised, and therefore potentially biased, surveys; spatio-temporal extensions; spatially structured zero-inflation. Throughout, we illustrate the methods with disease mapping applications that have arisen through our involvement with a range of African public health programmes.Comment: Submitte

Molecular epidemiology and diagnosis of "Mycobacterium bovis" infections in African cattle

Mycobacterium bovis is the major causative agent of bovine tuberculosis (BTB) and part of the Mycobacterium tuberculosis complex (MTBC). BTB can have an impact on the national and international economy, affects the ecosystem via transmission to wildlife and is of public health concern due to its zoonotic potential. Although still present in some industrialized countries, BTB today mostly affects developing countries lacking the resources to apply expensive test and slaughter schemes. In Africa, the disease is present virtually on the whole continent; however, little accurate information on its distribution and prevalence is available. Evaluations of antemortem tests for the diagnosis of BTB in Africa are scarce but a prerequisite to identify appropriate tools for future disease control programs. Spoligotyping and variable number of tandem repeat (VNTR) typing of M. bovis strains isolated from cattle in the UK has revealed the predominance of a single clonal complex of strains with subtypes of the complex being geographically localized to specific regions within the country. Spoligotype patterns of strains isolated from cattle from several countries throughout the world have been reported in a number of recent studies and the construction of databases (www.Mbovis.org, SPOLDB4) has facilitated their comparison and helped elucidate the distribution of specific strain families. In an attempt to gain insights into the population structure of M. bovis in Africa, we have isolated strains of M. bovis from cattle carcasses with gross visible lesions at abattoirs in Dakar (Senegal), Bamako (Mali), Sarh (Chad), Morogoro (Tanzania), Algiers (Algeria) and Blida (Algeria). These mycobacteria were subjected to spoligotyping and VNTR typing. A specific region of difference, which we have named RDAf1, was previously found to be absent in strains from Chad and presence of this deletion was assessed in our strain collection by PCR. In collaboration with others, additional strains of M. bovis from other African countries were subjected to molecular typing. At the abattoir of Sarh in Chad, 954 cattle were subjected to single intra-dermal comparative cervical tuberculin (SICCT) testing and two recently developed fluorescence polarization assays (FPA) prior to slaughter. Animal carcasses underwent standard meat inspection. Gross visible lesions were extracted, analyzed by microscopy and cultured. Cultured acid-fast bacilli (AFB) were further characterized by molecular techniques. The different diagnostic tests were evaluated using a sub-population of animals with either a PCR confirmed MTBC infection or no visible lesions. In addition, a Bayesian model for the evaluation of multiple diagnostic tests in the absence of a gold standard method was developed. In collaboration with others, we have identified a clonal complex of strains of M. bovis present at high frequency in cattle in population samples from Chad, Cameroon, Nigeria and Mali. This closely related group of bacteria is defined by the RDAf1 chromosomal deletion and can be identified by the absence of spacer 30 in its spoligotype pattern. We have named this group of strains the Bovis African1 (Af1) clonal complex. Strains of the Af1 clonal complex were not detected in population samples from other regions in Africa or other parts of the world, suggesting that the Af1 clonal complex is geographically localized to sub-Saharan West-Central Africa. VNTR typing allowed to distinguish sub-populations of the Af1 clonal complex, which were geographically localized to different countries. This was an unexpected result suggesting that the movement of strains between countries is not common in this area. In Mali, in addition to Af1, a second clonal group of M. bovis has been detected and matching VNTR patterns for some of its strains and strains from France could indicate a French origin. In Tanzania, also two clonal complexes of M. bovis were detected by spoligotyping with one clade showing a link to strains, previously identified in Uganda and Ethiopia and the second clade showing a link to strains previously isolated in South Africa. M. bovis strains isolated from Algerian cattle were closely related to strains from continental Europe and especially France. In conclusion, our work has revealed important insights into the population structure of M. bovis in Africa and suggests the presence of distinct clonal complexes of strains, geographically localized to specific areas of the continent. Our Bayesian model estimated the true BTB prevalence amongst the slaughterhouse cattle population in Sarh, Chad to be at 8%. The Bayesian and the gold standard test evaluation methods indicated that the ideal cut-off for positive SICCT test interpretation should be lowered from > 4 mm (OIE standard cut-off) to > 2 mm, in the Chadian setting. This result is of practical relevance and likely to apply to other countries in sub-Saharan Africa. Using this cut-off, sensitivity and specificity of SICCT was estimated at around 65% and 90%, respectively. Both FPA tests showed a sensitivity of less than 50% but specificities of at least 90%. Our results suggested that a substantial amount of lesions detected at the abattoir have been caused by other organisms than M. bovis

IMPACT OF TECHNOLOGY ON IMPROVING HIV AND TUBERCULOSIS HEALTH OUTCOMES AMONG AFRICAN COUNTRIES

This study used health analytics approach to evaluate the association between population health outcomes and Information and Communication Technology (ICT) infrastructures at a country level. This study used aggregate data obtained from the World Bank database, and the International Telecommunication Union (ITU) database for 53 African countries for the periods 2000 to 2016, and quantitatively explored the impact of ICT infrastructures’ diffusion on population health outcomes. ICT data was obtained from the ITU database. Similarly, population health attributes were retrieved from the World Bank database. ICT infrastructure variables used in this study include: internet access, mobile phone use, and fixed telephone subscriptions. However, population health outcome variables for this study include: HIV prevalence, access to antiretroviral therapy, Tuberculosis incidence, and mortality rates. Econometric study methodology involved a Dynamic Panel Model (DPM). Study findings showed that promoting ICT use among the public has opportunities for improving Tuberculosis (TB) and HIV health outcomes. However, the impact of each ICT infrastructures on improving TB and HIV health outcomes differed, which this study inferred to be as a result of different functionalities of the ICT infrastructures, as well as the peculiar features of the health outcomes studied. This study also did an Exploratory Spatial Data Analysis (ESDA) of TB treatment completion rates among health systems in Africa to help visualize trends and identify patterns, clusters and outliers. It evaluated spatial relationships between mobile phone use and TB treatment completion rates using differential local Moran’s I and bivariate Moran’s I techniques. Study result identified statistically significant positive autocorrelation values for the periods evaluated, as well as varying cluster patterns in TB treatment completion rates. The cluster patterns increased across the three-time periods among geographically referenced data evaluated in this study. This study also identified a direct relationship between mobile phone use and TB treatment completion rates among relevant African countries studied. Thereby, necessitating the need to strengthen national policies that promote TB and HIV medication adherence and completion using eHealth strategies among African health systems. Another important policy implication of this study for African governments is that investing in eHealth, including educating the masses on ICT use, could be an alternative policy to improve population health

Opportunities and challenges for modelling epidemiological and evolutionary dynamics in a multihost, multiparasite system: Zoonotic hybrid schistosomiasis in West Africa

Multihost multiparasite systems are evolutionarily and ecologically dynamic, which presents substantial trans‐disciplinary challenges for elucidating their epidemiology and designing appropriate control. Evidence for hybridizations and introgressions between parasite species is gathering, in part in line with improvements in molecular diagnostics and genome sequencing. One major system where this is becoming apparent is within the Genus Schistosoma, where schistosomiasis represents a disease of considerable medical and veterinary importance, the greatest burden of which occurs in sub‐Saharan Africa. Interspecific hybridizations and introgressions bring an increased level of complexity over and above that already inherent within multihost, multiparasite systems, also representing an additional source of genetic variation that can drive evolution. This has the potential for profound implications for the control of parasitic diseases, including, but not exclusive to, widening host range, increased transmission potential and altered responses to drug therapy. Here, we present the challenging case example of haematobium group Schistosoma spp. hybrids in West Africa, a system involving multiple interacting parasites and multiple definitive hosts, in a region where zoonotic reservoirs of schistosomiasis were not previously considered to be of importance. We consider how existing mathematical model frameworks for schistosome transmission could be expanded and adapted to zoonotic hybrid systems, exploring how such model frameworks can utilize molecular and epidemiological data, as well as the complexities and challenges this presents. We also highlight the opportunities and value such mathematical models could bring to this and a range of similar multihost, multi and cross‐hybridizing parasites systems in our changing world

Geostatistical modelling and survey sampling designs for malaria control and surveillance

In many low-and middle-income countries, malaria is endemic and remains a serious health threat and a significant contributor to mortality. According to the World malaria report 2019, more than ninety percent of malaria cases occurred in Africa. Children remain the most vulnerable group. Cameroon belonged to the list of countries most affected by malaria. The country is subdivided into several ecological zones in which malaria prevalence is spatially heterogeneous. The disease transmission is highly seasonal in the North, perennial in the southern and eastern parts and relatively low in the high mountains of West and Adamawa regions. Substantial efforts made by international donors and the Cameroon government led to a decline in the malaria burden over the last decade. Many health interventions and actions were implemented to fight against the spread of disease. National surveys such as the Demographic and health surveys (DHS), malaria indicator surveys (MIS), and multiple indicator cluster surveys (MICS) are conducted every three to five years collecting individual and household level data on malaria, disease interventions and socio-economic factors to measure the progress achieved in the control of the disease. The data are georeferenced at the centroid of clusters consisting of groups of around 25 households. For confidentiality reasons the cluster coordinates are reported with jittering, that is they are misplaced within a buffer around the actual location. Bayesian geostatistical models are commonly used to predict the geographical distribution of disease prevalence and quantify the effects of interventions. Predictions are improved by including in the models climatic and environmental proxies available at high spatio-temporal resolution from remote sensing sources. Despite the large number of survey data that have been analysed using geostatistical models, there are few studies of the effects of survey design factors on model-based estimates. The overall goal of the thesis is to evaluate and further strengthen the methodology of the malaria survey designs used for disease monitoring and evaluation, in particular the aspects related to the timing of the survey, the jittering in the coordinates of the reported cluster locations, and the seasonal monitoring of malaria data. Furthermore, we evaluated the ability of the survey data to estimate the malaria-related deaths and compared estimates of the effects of malaria interventions using data from malaria surveys and the Health Management Information System. More specifically, the thesis pursues the following objectives: i) assess the influence of the survey timing by comparing the DHS and MIS geostatistical model-based malaria risk estimates obtained at different malaria transmission seasons; ii) assess the effects of the DHS jittering algorithm on the prediction of malaria risk and on the estimates of the disease risk factors; iii) evaluate the effects of malaria interventions on the geographical distribution of disease incidence after adjusting for the effects of climatic and environmental factors; iv) improve malaria disease and vector survey sampling designs by optimizing the selection of survey locations and v) evaluate the ability of malaria surveys to estimate malaria-related deaths. The above objectives were addressed by analysing DHS, MIS and MICS survey data from Cameroon as well as malaria incidence data from the Health Management Information System. The methodology and results for each objective are included in five main chapters. In Chapter 2, Bayesian stationary geostatistical model are employed to analyse MIS and DHS, national surveys conducted in 2011 during the rainy and dry seasons, respectively. Geostatistical variable selection was applied to identify the most important climatic factors and malaria intervention indicators. The results showed that the timing of the malaria survey influences estimates of the geographical distribution of disease risk, especially in settings with seasonal transmission. In countries with different ecological zones and thus different seasonal patterns, a single survey may not be able to identify all high-risk areas. Chapter 3 presents the influence of jittering on the assessment of intervention effects and the spatial estimates of disease risk distribution at high spatial resolution. Based on original MIS cluster locations, a set of a hundred (100) shifted cluster locations were generated using the DHS jittering algorithm. Bayesian variable selection applied in the original dataset locations as well as for each one of the hundred jittered datasets to select climatic/environmental predictors, socio-economic factors and malaria intervention indicators. Geostatistical models were applied to the original as well as the simulated data using the selected covariates. The results indicated that the selections of important climatic predictors and of intervention indicators were influenced by the jittering, while estimates of the disease risk at high geographical resolution were slightly affected. Chapter 4 focused on the selection of relevant cluster locations for an efficient assessment of intervention effects. Based on MIS data, a Bayesian geostatistical model was applied to the most important climatic predictors to estimate the malaria risk, and associated uncertainty over a high resolution gridded surface across Cameroon. An adaptive algorithm was proposed to select survey locations based on a multi-criteria objective approach. The adapted algorithm was able to identify a meaningful subset of cluster locations based on their contribution to the uncertainty and the needs of the national malaria program. In chapter 5, we evaluated the effects of interventions on the spatiotemporal dynamic of malaria incidence and the capability of HMIS data to capture disease pattern. From 2012 to 2016, confirmed malaria data were extracted and aggregated by month at the district level. During the same period, climatic factors were obtained from satellites and averaged over the district surface. Bayesian variable selection was applied to identify the most important lag time for each continuous climatic factor. A Bayesian spatiotemporal of the relationship between malaria incidence and intervention was fitted and adjusted with the important climatic factor. The percentage of households having one ITN per two persons was identified as the most important coverage indicators, while the normalized difference vegetation index, rainfall estimates were selected among climatic predictors. Having an ITN for every two persons was negatively associated to malaria cases. The incidence maps drawn at district level were able to capture patterns of disease risk that were not estimated by the DHS and MIS data. Chapter 6 assessed the relationship between malaria prevalence and all-cause mortality in infants and in children under-5 years old, by considering seasonal influence of malaria transmission as well as socio-economic factors. Bayesian geostatistical Bernoulli and zero-inflated Bernoulli models were fitted on the mortality risk data. A statistically important relation was estimated between infants (excluding neonates), under-five years old mortality and malaria risk. The effects of malaria parasite risks on under-five mortality became more statistically important in the absence of neonates. Mortality in the under-five group was reduced during the dry season

Comparison of a Flow Assay for Brucellosis Antibodies with the Reference cELISA Test in West African Bos indicus

Brucellosis is considered by the Food and Agricultural Organisation and the World Health Organisation as one of the most widespread zoonoses in the world. It is a major veterinary public health challenge as animals are almost exclusively the source of infection for people. It is often undiagnosed in both human patients and the animal sources and it is widely acknowledged that the epidemiology of brucellosis in humans and animals is poorly understood, particularly in sub-Saharan Africa. It is therefore important to develop better diagnostic tools in order to improve our understanding of the epidemiology and also for use in the field for disease control and eradication. As with any new diagnostic test, it is essential that it is validated in as many populations as possible in order to characterise its performance and improve the interpretation of its results. This paper describes a comparison between a new lateral flow assasy (LFA) for bovine brucellosis and the widely used cELISA in a no gold standard analysis to estimate test performance in this West African cattle population. A Bayesian formulation of the Hui-Walter latent class model incorporated previous studies' data on sensitivity and specificity of the cELISA. The results indicate that the new LFA is very sensitive (∼87%) and highly specific (∼97%). The analysis also suggests that the current cut-off of the cELSIA may not be optimal for this cattle population but alternative cut-offs did not significantly change the estimates of the LFA. This study demonstrates the potential usefulness of this simple to use test in field based surveillance and control which could be easily adopted for use in developing countries with only basic laboratory facilities