Spatio-temporal modelling and analysis of spatial accessibility to primary health care: A case study of Bhutan

Both spatial and aspatial dimensions of healthcare system are important in strengthening the healthcare system of any country. Knowing the spatial aspects of healthcare accessibility can help develop proper health policies in planning equitable allocation of health resources across the country. This thesis deals with the modelling of population and spatial accessibility using GIS, and an analysis of spatial and temporal changes in accessibility to healthcare services in Bhutan

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Sequence Diversity, Evolution and Transmission of Influenza A(H1N1)pdm09 and A(H3N2) Viruses in Kenya, 2009-2018

Background The global surveillance of human influenza viruses has resulted in the generation of a uniquely extensive collection of geographically and temporally comprehensive virus sequence data, which has provided an opportunity to explore the drivers behind the global spread of influenza viruses. However, due to the insufficient spatiotemporally representative virus sequence data from tropical and sub-tropical African countries, especially from sub-Saharan Africa, relatively little is known about the possible role these regions play in the global spread of influenza viruses. Using influenza A(H1N1)pdm09 virus and A(H3N2) virus sequence data, this study aimed to understand how seasonal influenza viruses are introduced and spread across geographically defined regions, whether local, national, continental or global, and their patterns of persistence across these regions. Methods A laboratory method for whole-genome sequencing (WGS) of influenza A(H1N1)pdm09 and A(H3N2) viruses on Illumina next-generation sequencing (NGS) platform was established at Kilifi, coastal Kenya. This was then used to sequence samples collected between 2009 and 2018 from geographically defined regions: local community in Kilifi (n=66); countrywide in Kenya (n=383); and across Africa from 5 countries (n=100). The arising genomes were analyzed using phylogenetic and phylogeographical methods to investigate the patterns of spread, persistence and fade-out of seasonal influenza viruses at a local community in Kilifi, countrywide in Kenya, and across the African continent. Additionally, a global contemporaneous sequence dataset was analyzed in conjunction with the WGS data from this study in a Bayesian framework for inference of the situation of sub-Saharan Africa in the global network of spread of influenza viruses. Results A total of 549 new influenza type A virus (IAV) whole genomes were generated during this study; 414 A(H1N1)pdm09 virus and 135 A(H3N2) virus genomes. Phylogeographical analyses revealed that local seasonal community epidemics of IAV were initiated by multiple independent introductions into the community, with each introduction commonly spreading to multiple locations within a relatively short period of time. Countrywide, in Kenya, circulation of IAV was predominantly characterized by virus migration from multiple locations to multiple destinations within the country and between locations in proximity; persistence of IAV countrywide might therefore be modulated by frequent virus introductions from outside the country and virus spread between locations in proximity. Continentwide, strains of IAV from Africa fell into strongly supported multinational lineages that suggested possible intra-continental spread of influenza viruses within Africa, which exhibited a significant northern to southern hemisphere migration. Globally, significant migration pathways from multiple geographical regions to multiple geographical destinations that also includes Africa were observed, which suggests that the seeding of epidemics of influenza viruses globally is driven by different geographical regions that also includes Africa. However, East or Southeast (E-SE) Asia acted as the major source of spread of influenza viruses globally, which is consistent with findings from other studies on the global circulation of influenza viruses. A greater global migration was observed for A(H3N2) virus compared to A(H1N1)pdm09 virus, consistent with greater global migration of A(H3N2) virus compared to (H1N1)pdm09 virus. Conclusions The global migration dynamics of seasonal influenza viruses are well understood, and several models have been proposed to describe these patterns. However, analysis of virus sequence data from understudied regions, as exemplified in this study, suggests that these migration patterns are far more complex than those proposed by current models alone. For example, the findings from this study support the notion that influenza viruses persist as temporally structured migrating metapopulations in which new virus strains can emerge in any geographical region, including in Africa, with the location of the source population changing regularly. The epidemics across geographically defined regions (local community, countrywide, continentwide, and globally) are also interconnected at various scales of observation to different extents. Therefore, a more complete understanding of the global migration dynamics of influenza viruses requires deeper and wider sampling of viruses from understudied tropical and sub-tropical regions, notably, Africa, South and Central Asia, and South America. Understanding the circulation patterns of influenza viruses across geographically defined regions, together with their origins and patterns of persistence, is useful in selecting the most effective vaccine strains for the circulating seasonal influenza viruses. The rapid and widespread global mixing of viruses from all northern and southern hemisphere countries including in countries in Africa, Asia, Europe, North America, South America, and Oceania as reported in this study emphasize that global vaccine recommendations need well distributed, widespread global influenza A(H1N1)pdm09 virus and A(H3N2) virus sampling from as many localities as possible