“Risk Zoning of Bangladesh in Covid-19 Situation”

The daily updates of the newly confirmed Covid-19 cases portray a very generalized image which, at a glance, depicts a worsening situation in every districts of Bangladesh. However, if considered on a smaller scale of district level, the scenario is not that bad. It can be seen that only a few districts have uncontrollable spreading where transmission is untraceable or management is failing. Maximum districts are overall, handling the situation well enough given their limited resource and services.

We have visualized a map categorizing the whole country of Bangladesh based on resulting risks zones with their following values. The risk zones are selected from classification criteria discussed below.

Check out the interactive version of the map in our covid-19 dashboard: http://groupmappers.com/covid19/

The legends in the map show the classes in the order- Handling well so far > Need to manage better > Still retrievable by social distancing > Untraceable outbreak > Worst case scenario, where ‘Handling well so far’ is the best possible situation and ‘Worst case scenario’ is the worst one. The indexing of the zones is done from criteria stemming from plots generated by COVID-19 confirmed case data. The plots show Bangladeshs’ district wise trajectories in confirmed COVID-19 cases from March 7 to June 3. These show daily new cases of COVID-19 vs time, where the cases per day are averaged over the 3 separate averages i.e. 3-day rolling average. 5-day rolling average and 7-day rolling average of the number cases respectively. The ‘case’ axis (y-axis) is exclusive to each curve based on their highest confirmed case.

Criteria of classification:

We have selected the zones following mainly 2 criteria from the plots, they are as follows:

Index Criteria 1:
Highest number of cases in that district in a day within the given timeline
Criteria 2:
Trend of the curves (focusing on terminal point)
1 <20 Downward or Flat Curve within 15%
2 21-50 Curve within >15% – 25%
3 51-100 Curve within >25% – <50%
4 101-200 Curve at >=50%
5 201-1000 <20% Cases from the Peak

Thus, each district would have 2 indices, one for each criterial. We then weighted those 2 criteria by the indices to get 1 value for categorizing. Finally, we named our categories or, Risk Zones as per the list:

Index value after weighting the criteria Risk Zone
2 Handling well so far
3-4 Need to manage better
5-6 Still retrievable by social distancing
7-8 Untraceable outbreak
9-10 Worst case scenario

Criteria 1: The plots show classification of districts according to the highest number of cases in a day within the given timeline

Highest number of cases in a day within the given timeline ➡ <20 (i)

Highest number of cases in a day within the given timeline ➡ <20 (ii)

Highest number of cases in a day within the given timeline ➡ 21-50

Highest number of cases in a day within the given timeline ➡ 51-100  

Highest number of cases in a day within the given timeline ➡ 101-200

Highest number of cases in a day within the given timeline ➡ 201-1000

Criteria 2: The plots show classification of districts according to the trend of the curves mainly at the terminal point, i.e. for last recorded data

Districts with downward or flat Curve within 15%

Districts with curve within >15% – 25% close to the end

Districts with curve within >25% – <50% close to the end

Districts with curve at >=50% close to the end

Districts with <20% cases from the peak close to the end

FREQUENTLY ASKED QUESTIONS

Why Not Show Cases Per Population?

Cases per population is important to understand how well the district administration is managing the situation. However, when it comes to plotting the curve of a district, the number of new cases per day has the same denominator for all the points in that curve. Thus, the relative values for plotting the graph remains unchanged. As we have plotted for each district considering their respective cases other than normalizing all of them, the curves are error free yet comparative.

Why Are All of the Peaks the Same Height?

The plots are adjusted in a way that 100% of each one will be visualized on the same height. As stated before, the ‘case’ axis (y-axis) is exclusive to each curve based on their highest confirmed case. So, the curves are unique to each district. But, the percentage of changes e.g., declination or inclination or horizontal trend of the curves in the same criteria will be same. We see that, x% refers to the (x/total population) X 100 of that specific district. Thus, for countries in the same criteria, the points depicting the same percentage will be on the same height, so will the peaks.

 

Data Source: IEDCR, DGHS

Project inspired by EndCoronavirus.org