The NIMR-MDB can be used locally without setting up a server or can be made available to all by hosting it on a certified online platform such as ‘shinyapps.io’ at affordable costs. Though WHO's District Health Information System (DHIS2) is available for use, India has not subscribed to it therefore there is no digital dashboard being used in India to the best of our knowledge. Our NIMR-MDB is a fully interactive, flexible, and user-friendly program for rigorous and rapid analysis of malaria epidemiological data. Therefore, there is a need to have a unified platform like the NIMR-MDB which is tailor-made according to the Indian health structure and caters to the understanding of malaria epidemiology in India via integrating all available malaria data. The third graph on this Tab is a district-level geographical map of the selected state, and it displays the selected malaria parameter in a given year using a graded colors map ( Figure 4-B).Ī large quantity of data related to malaria are being collected every year in India at various levels like state, district, community health centers (CHCs), primary health centers (PHCs), sub-centers (SCs), and district hospitals. The second graph on this Tab is a Box-Whisker's plot of any given malaria parameter for all the districts of the selected state for all years (say from 2000 to 2019) ( Figure 4-A). Then a bar graph of all the districts of Assam, arranged in descending order of API for the year 2007, would be displayed in the right-hand panel ( Figure 3). For example, a user may select Assam as a state of interest, 2007 as the year, and API as the malariometric parameter using the three drop-down menus on the left-hand panel. The first graph, displayed on the right-hand panel, is a bar diagram that arranges all the districts of the selected state in descending order of chosen malaria parameter in a particular year (determined by the user). As in the previous Tab, a user can also select multiple options using the drop-down menus provided in the left-hand panel such as the state, year, and the malaria variable of interest. Tab D2: On Tab D2, users are provided with three different graphs that compare and contrast various malaria parameters across all the districts of a selected state. Therefore, an efficient tool for analyzing malaria data is needed and a digital dashboard can effectively solve the above-mentioned problems. The process might become more cumbersome if the user intends to compare the data between several districts as India has >720 districts. For example, if one is interested in analyzing the Annual Parasite Incidence (API) of a particular district in India over ten years, one would need to extract the information from ten excel files (one file for each year in the present scenario). Although NCVBDC's malaria data are rich, their analysis can be tedious. The epidemiological data are also valuable for (a) calculation of appropriate sample size, (b) finalizing the duration of any study, (c) determining the transmission periods, (d) and for identifying the dominant malaria parasite species, amongst others. Understanding the malaria-metric data of a particular region is a prerequisite for effectively designing any operational study. The variability and heterogeneity in malaria parameters in a vast country like India requires a highly granular approach for analyzing malaria incidence and planning appropriate interventional tools. This is one of the most extensive data sets on malaria and its ready analysis is vital for constant monitoring of the malaria situation in India. The Lancet Regional Health – Western Pacific.
Īs a bonus, you can verify this by viewing your app in a browser and right-clicking and selecting “view source.” You’ll see the same source for both approaches above, but not with the original file-based method.įor almost every Shiny app, the difference between including CSS via inlining and with a link is negligible, and there is no need to worry about performance implications. * Get a fancy font from Google Fonts */ url('') body " )) ), titlePanel ( "Old Faithful Geyser Data" ).