Computing the Exit from Covid-19 Lockdown

The Covid-19 lockdown required a cessation of all non-essential physical interaction, so as to minimise the transmission of the novel coronavirus between humans. Governments all over, want to curtail the rampant spread of the virus that mainly transfers via respiratory droplets, needing physical proximity between its victims. Indirect transmission, by way of fomites, is not fully documented but any community spread would mean as much.

Ending the lockdown means relaxing the restrictions on non-essential physical movement of people. It does not mean the end of the virus, and all precautions of keeping physical distance from others, handwashing, etc. would need to continue (this highly contagious virus has no cure or a vaccine, yet).

Relaxing the restrictions on such movement, will need to be calibrated depending on situational analysis in areas. A data driven approach could be possible, keeping it simple and with transparent, so that the general public can be willing participants. A simple risk assessment can be attempted by correlating the following geospatially defined data-

1. Geographic area of a population cluster
2. Current population in that area
3. Active cases of Covid19 in that population
4. Risk factor for unknown asymptomatic cases of Covid19
5. Vulnerability factor in the area (health, age, education, living conditions)

(4 and 5 can initially be subjective, but in future, can be more accurate with more data from testing for virus and of anti-bodies in asymptomatic cured is at hand)

Now, consider an urban zone with a population of 3 million in an area of 157 square kilometres, and having 206 active cases of Covid19. The risk, from asymptomatic hidden cases and for demographics, can be taken as 10x. The probable case load becomes 10 times 206, i.e. perceived threat of 2060 coronavirus carriers present in this region.

Simple mathematics will translate this into a circle of incidence; that is, from any given location in this populated area, there exists the probability of encountering a Covid19 carrier within a radial distance of 156 metres. Precautionary restrictions on movements have to match this assessed radius risk (data is of District of Mumbai City, a hotspot).

Examine now, say the region of Himachal Pradesh, a hilly rural state, with a population of 8 million, area of 55,673 sq.kms and with 11 known active cases of Covid-19.

Here, keeping the same risk multiplier of 10x, the radius of risk, or probable incidence of encountering a Covid19 carrier, is 12.7 kilometres. With granular data, more exact assessments can be done for the individual districts within H.P.

Both examples above, output different radius, for the same risk of exposure to residents of each area. In other rural areas, the radius of incidence could be bigger. In specific zones, where a workplace (shop) or a farmers field lies well within the assessed radius of risk, maybe normal functioning and travel albeit limited to the radius could be admissible.

Furthermore, if a new coronavirus case is not detected in the last 14 days of a future 21 day period, the risk factor can be reduced by half and so on, until there are no more cases in the country. The local population will get a target to aim; to increase their radius of freedom by not breaching an ever widening radius of risk, by minding each other to take the precautions so that no fresh cases can occur. If a new case is found, the radius will reduce.

A calibrated approach must also take care when the radius of travel overlays adjoining areas with a higher threat perception. The virus does not know man-made geographic boundaries. Therefore, any relaxation would need to include a buffer zone if coinciding with a higher level of Covid19 threat. Risk circles can be structured into categories for this purpose. Other complications can be added such as availability of hospital beds, equipment, doctors and nurses in the region. Luckily, in India, a national level plan can be initiated and coordinated.

Keeping it simple will help the public understand and participate. Anyone up for the mental calisthenics. A geospatially mapped color coding common area of influence (risk), modeled to indicate corridors of movement in overlapping zones can be envisioned.

Such a map could be boundaried by districts for ease of administration - be updated with every new case or as number of cases drop. Radius of risk or non-essential movement would be adjusted accordingly. Risk factor would be varied as per ratio of existing case to new cases within each 14 day period at aggregate national level or local.

Meanwhile, all essential movement, relating to the supply chain of agricultural produce and other basic goods, would need to continue irrespective of such threat assessments. For these tasks, other recommended precautions to safeguard the operators and to minimise risk of transmission of the virus would continue to be followed.