Hotspot Analysis geospatial epidemiology data analysis of disease in QGIS

Hotspot Analysis geospatial epidemiology data analysis of disease in QGIS

Hotspot Analysis geospatial epidemiology data analysis of disease in QGIS

Qgis in epidemiology surveillance. Click here to learn.

ladies and gentlemen so today I would like to discuss and also give you some in depth how this analysis been carried out so this is the example of special epidemiology this on the disease II in the Kuantan district 110 district is one of the district in Malaysia

it is a main capital area for Pahang state of Malaysia so this is the 2015 2016 2017 2018 2019 and also overall so we are going to learn about how to produce this kind of table this kind of map and see the distribution by year okay so actually if you got the data for daily it’s quite comprehensive because you can analyze the movement of the spreading of the disease is either been contained or not being contained inside the well control or not well control okay without further ado let us proceed with the so first of all you need to install the qgs this is an open software for manipulations of any geospatial element or just special component so I have here content district as one of the main layer okay this is the quantum district in Pahang state of Malaysia and then if you overlay the contents trick here is actually being reported using wgs84 or epsg 40 4 3 2 6 so you can see here this is the attribute not that about the name of the McKim we got the name but for the interest of time I’m just going to spell it as smoking 1 2 3 4 5 ok so how we obtain this kind of map actually being discussed with the previous video you can follow through our playlist to learn about how this map can be produced okay after that we proceed with the Contin grid so this is the example of the grid and the Contin grid actually contain the left bottom right and top name of each of them ok well so that’s that’s actually function like this if you select a certain part of the area you can already know not that some area already being highlighted here okay

let’s say you want to plot something ok so the next interesting part in our analysis is actually we want to know the disease because we want to map the disease according to this script why I created this greener ok if you find out more about this you can see that the muqam is actually being measured or this square grid i’ve been created about one kilometer away so we sort of project one kilometer each site whether the disease distribution is being spread evenly or not evenly so on because we need to do the special assessment in the next video or the other software we can call it as Judah so after you create this grid then you may want to proceed to import your data set so my data set is consists of longitude and latitude of the cases male or female reported to be happening according to the yes okay if you find out the raw data will be like this this is the raw data we got the latitude longitude years and also gender so what we need to do actually we need to import that layer also as a delimited text

Hotspot Analysis geospatial epidemiology data analysis of disease in QGIS

layer okay we need to find out where is the location of our layer and then it is inside the Kuantan example this is the disease a and automatically we got the long eternal it it already been specified by qgs and then we can add accordingly so after you add the disease a will be distributed evenly in your layer according to the dot here so you can see the dot so now we want to accumulate this dot inside those boxes okay because we want to count the boxes as how many cases happening in the area we can sort of proceed with the heat map straight we also can but for the purpose of this analysis i may not want to use that one so i’m just going to share with you about what kind of cases they’re already being tabulated so this is the cases they’re already being tabulated based on the disease e so this mean we calculate the disease a and then ask the computer to recognize those cases inside those small boxes okay so if you open this attribute you can see the case of 2015 2016 and then you can go a to till 2019 so we want to see the movement of the disease spread okay so the next part also that is very interesting to show to you is actually the other variable that

we can filter okay you can also include the case for female gay this case for female is actually you can filter accordingly according to the different years because you want to know whether the female located at where which area okay so let us make some idea so basically the disease a here will undergo the filtration okay you can just go to the filter here and then let’s say I want to specify gender is equal to lucky means that male and years I want to specify 2015 okay so this is it’ll be wrong it’s equal to 2015 okay after you did that what you can see here is actually the points is already being reduced and we want to count the point here inside those polygons you can use this vector analysis we call it as count point in a polygon so that’s mean the polygon will straight we having the numbers of cases okay so after you finish overall the analysis and specify each of the cases according to the different location here you can overlay eat and then make the computer under student each of the boxes contain one informations of the disease spread so probably maybe one boxes contain more than one okay let’s see this area okay you can see that this area contain at least two disease in this pest free area and for those who are under good the process of the mitigation or control you can actually make this layer a little bit effect so that you can understand the locations that we are talking about here okay so actually if I’m not mistaken this is the the airport area the airport area okay so this is the location indicate two pieces in this area so you can see we spread quite evenly in this locality for 2015 so you can see whether those spread influencing over time over the period of time to spread even more

further away okay so to simplify everything and then I can just proceed with another software we call it as Yoda because Yoda is very useful for me to understand the spread

of the disease according to the different spatial elements so if I insert it here so we got the case this is the overall case that’s mean male and female are accounted for so we can specify the projection or the trajectory of the spread according to the different years which just need allocated more resource than the other yes we can do that by specify the exploratory spatial data analysis first so I’m going to use the map custom break we are going to create one so I’m going to specify the case for 2015 and I’m going to do it by sequential according to the two categories sorry I’m going to make it as a three categories okay it will produce some error notation but it’s okay so I’m going to specify a spec number one is one and then the bridge number two will be two okay and then you specify it back alright so the computer will be understand less than one or zero no cases will be reported with this color followed by one or two cases will be reported in to discolor and more than two cases will be projected using this color so after you done doing that automatically actually the Jodha are going to specify to you already the number of cases so you can see the number of cases perhaps is one of your interest is the darker the darker area here okay this area this area and this area the weakness of using the juda of course you cannot see in that what is the location what is the area but that being specified therefore the qgs might going to help you to specify what kind of area are we talking about in terms of the opens my google satellite or anything with regard to the location name then you can see here we got four areas indicate more than two cases and then we can got around seventy three locations and for that one or two cases okay so you can just select that one and right-click over here and move selected to the top so automatically the Jodha are going to specify those areas for you for the 2015 cases so it’s very helpful and it’s very useful for you to understand which area that you need to give more priority in terms of the risk management and the service assessment okay so that is one example so another example will be we are going to find out the hot spot analysis using the space analysis we call it as a bi where actually you call it as a univariate local modern sigh but you cannot done it because you need to specify the weight therefore we need to load or create the weight so in this case I already have the weight and then the weight is already mentioning here and then we can produce the space univariate local models i we can request for 2015 data and we can ask the computer to produce the significant map and also significant Lisa Lisa is local indicator for spatial analysis all right so you can see here the spot map indicates the high high area which is the hot spot area indicated here where by the high-low which is the high number with the low cases in the neighboring region is actually 25 so this graph or local models are graph is very crucial especially if you want to identify the hot spot cold spot and also special outlier these two region is actually special or are you this one is low high this one is high low low high means that the cases indicated to that particular area is actually low as compared to the neighboring region is considered to be high oh you can look into this high area where it is concentrated with the high cases whereby the neighboring region is actually very low so because this two component in my professional judgment will say that is very important for you to take it up some information out of it because you want to further analyze and also that they understand the behavior of the people over there to understand the control mechanism taking place or not and so on okay so one of the major

component that we might want to investigate in the high-low or spatial outline is actually this area you can see here 2015 we we want to make sure that 2016 will be no cases here otherwise the disease is not being continued no you see so the disease actually can spread through if one can move away to here this is another state so that is one kind of analysis that is very crucial in decision-making procedure because we want to manage our team and we want to manage our resources to the appropriate areas okay so that’s why we conduct these local models i statistic to find out any statistical difference or statistical significant in the particular species or you can also conduct the bivariate local models i means that we embark and not the variable bivariate means that two variables or you can choose for the

differential local models are looking for the different time in place which is this is called spatial temporal epidemiological modeling we’ll be talking later on okay so I think that some of everything about the analysis of this partial element you can play around with this data because it is that anyway is a fixed teachest data is not true data it’s just training data for my students so that they already understand and know about how to consider the movement of the disease especially in spatial epidemiology surveillance system okay so the special element is very important in the end of the nutshell is very important because we want to estimate everything with regard to the intelligence that we have okay that’s all for me see gain for the next video for those who are first time coming to my channel please do consider to the three point three thing the first one please do consider to like this video if you think that you like it and then the second one please do consider subscribe and the third please do consider share it to your friends families and also on your clique and members and if you find out that you need more focus and analysis and ideas for how we can connect this and insist please let me know so that we can conduct a series of web Mina free of charge let me share with you for the information and give me some ideas for there as well especially the