Geographic information systems applications in India's public health are we moving towards the right direction?

Governments worldwide focus particularly on digital healthcare sensors for leveraging data and technology like Geographic Information Systems (GIS) to improve governance and service delivery. Geoinformatics technology can help with epidemiological research and outbreak response, minimizing the health consequences in communities beforehand, during, and then after epidemic episodes. We can all agree that location and time play a crucial role in carrying out an efficient public health response. Since location information is essential for every stage of planning, response, and recovery, GIS helps the location-based support of public health preparedness programmes like support for decisions, resource allocation, communication and collaboration, and civic participation. GIS scales to situations ranging from adverse weather to pandemics. Public health professionals can coordinate their efforts with those of other organizations and external stakeholders due to maps and apps. The public health preparedness community may achieve significant strides by incorporating GIS data, models, communication and engagement centres, and location-centric apps. GIS technology can help with this efficient method for gathering data, performing analysis, where they are most needed, interacting with decision-makers, and finally achieving health equity can be created with the aid of a location-based strategy. During COVID-19, this reality was disseminated more extensively through the news media and the national, state, and local governments. This paper evaluates the application of GIS in the Indian public health system and the various aspects of public health where GIS may emerge as a game-changer for future policy decisions.

Geospatial Modeled Analysis and Laboratory Based Technology for Determination of Malaria Risk and Burden in a Rural Community

Introduction:Geographical Information System (GIS) has proven to be very useful for large scale mapping of ecosystems, land use and cover, disease prevalence, risk mapping and forecasting. GIS establish relationship or link between vector borne diseases and associated environmental factors thereby providing explanation for spatial distribution pattern, possible causes of diseases outbreak andimplications on the community.Aims and Objectives:Our approach in this study was to define and identify areas and places that are exposed to Malaria risk through proximity analysis and to compare geospatial risk with laboratory diagnosed malaria epidemiology. Methodology:Garmin GPS was used to capture the geographic coordinates of six (6) selected settlements and overlaid with georeferenced and processed satellite images in the study area. GIS modeling was performed on risk factors using weighted overlay technique to produce malaria risk map. A total of One hundred and thirty-five (135) vulnerable individuals were diagnosed for Malaria with light Olympus microscope and rapid diagnostic kit (RDT). Data were entered and analyzed using R-Package for Statistical Computing and Graphics.Results:Proximity to malaria risk follows relatively the order Apodu > Central Malete > Elemere > KWASU Campus > Gbugudu. Apodu being the largest place with proximity to malaria risk, within 500m radius. The risk index increases as one move away from the center of the settlement. The possible explanation for this high risk could be the presence of pond / lake in Apodu. This is a good breeding site for mosquito couple with dense vegetation as one move away from the centre of the settlements. Unlike Apodu, Gbugudu was at medium risk at 100m buffer (60%) but the risk index decreases as one move away from the settlement centre. The absence of thick vegetation and presence of numerous open farms and partly cultivated farmlands on the eastern part could have been responsible for reduction in risk index. Dense vegetation and ponds were observed within Apodu, while Central Malete was built up with dense vegetation are possible reasons for the high-risk index, while settlements within 1 km radius around KWASU campus recorded lower risk index possibly dueto low vegetation. The geospatial malaria risk analysis correlates with the laboratory-based test results. RDT kits and light microscopy results showed Apodu having the highest malaria prevalence with 46% and 58.7% followed by Elemere 41% and 30.3% respectively. When calculating prevalence by aggregating results across all communities, Apodu still had the highest malaria prevalence for the whole region. RDT and light microscopy results combined for all communities had Apodu with malaria prevalence of 21.48% and 27.4% followed by Elemere with 11.85% and 12.5% respectively. Gbugudu had the least malaria prevalence within the region with 3.7% and 7.4% respectively.Discussion and Conclusion:Findings of this study showed dense vegetation and ponds within Apodu, Elemere and Central Malete served as good breeding site for mosquitoes and were responsible for the high-risk index at these areas. Settlements within 1 km radius around KWASU campus recorded lower index possibly due to low vegetation. Results from this study indicate that the degree of malaria parasitaemia in the three major settlements correlates directly with the remote sensing data

Modelos jerárquicos espacio temporales para mapear riesgos relativos de dengue, en el Municipio Girardot, Estado Aragua, Venezuela / Spatio-temporal hierarchical models for mapping relative risks of dengue in the Municipality of Girardot, Aragua State, Venezuela

Los modelos Bayesianos jerárquicos espaciotemporales han sido usados en el mapeo de enfermedades, estudios de contaminación ambiental, contaminación industrial, entre muchos otros. Bajo esta metodología, los datos están asociados con un punto en una localidad E y con un instante de tiempo t. El objetivo de este trabajo es modelar el riesgo relativo de contraer dengue en el municipio Girardot del estado Aragua, Venezuela, durante el periodo epidemiológico del año 2009. Se proponen tres estructuras de modelos, un Binomial que toma en cuenta la variabilidad en el conteo de la ocurrencia de la enfermedad en las parroquias del municipio. Una segunda propuesta incluye un modelo Binomial como primer nivel de jerarquía, más un segundo nivel que introduce el efecto espacial, el efecto temporal y la interacción espacio-tiempo. Finalmente, un tercer modelo espacial que combina el modelo Poisson en el primer nivel de jerarquía para el número de casos, y en el segundo nivel de jerarquía se relaciona el riesgo relativo con las covariables a través de la función logaritmo más un efecto aleatorio. Los datos fueron recopilados por semanas y clasificados de acuerdo a las parroquias del municipio. Se utilizó el criterio de información de deviancia (DIC) para seleccionar el mejor modelo, resultando el modelo Poisson el más adecuado para representar el riesgo relativo de contraer dengue en la zona bajo estudio, confirmando que los patrones de alto riesgo se encuentran en las parroquias ubicadas al sur y suroeste del municipio Girardot, colindando algunas de ellas con el lago de Valencia.

Hierarchical Bayesian space-time models have been used in the mapping of disease, studies of environmental pollution and industrial pollution, among many others. Under this methodology, the data is associated with point in a locality E and an instant in time t. The aim of this work is to model the relative risk of dengue in Girardot Municipality, Aragua State, Venezuela, during the epidemic period 2009. In that sense, we propose three models. First, a binomial model that measures the variability in the count of occurrence of the disease in the parishes of the municipality. A second model includes the binomial model as a first hierarchical level, plus a second level which introduces the spatial effect, the temporal effect and spacetime interaction. Finally, a third spatial model that follows a Poisson model at the first level of hierarchy for the number of cases, and in the second level of hierarchy relates the relative risk associated with covariates through the logarithm function over a random effect. Data were collected for weeks and classified according to the parishes of the municipality. The Deviance Information Criterion (DIC) was used to select the best model. The Poisson model was best suited to represent the relative risk of contracting dengue in the area under study, showing that high-risk patterns were found in the parishes located in the south and southwest of the Girardot municipality, some of them bordering the lake of Valencia.

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socioeconomic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense vegetation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain's water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9 percent accurate.