eassy on effects of lockdown in different fields
Answers
Explanation:
The Indian government argues that lockdown has successfully reduced the spread of the novel coronavirus epidemic, while some critics argue that it has largely failed. Where does the truth lie? What exactly does the available data coupled with modelling tell us?
The politics of lockdown
The effects of lockdown on disease cannot – and should not – be looked at in isolation. They are entwined with its political and humanitarian effects, including unemployment, hunger, an unprecedented migrant worker crisis, and widespread loss of access to healthcare. These crises could have been averted or lessened with planning, but they are now an essential part of India’s lockdown story. So what might have motivated the hasty decision to lock down
Explanation:
The Indian government argues that lockdown has successfully reduced the spread of the novel coronavirus epidemic, while some critics argue that it has largely failed. Where does the truth lie? What exactly does the available data coupled with modelling tell us?
The politics of lockdown
The effects of lockdown on disease cannot – and should not – be looked at in isolation. They are entwined with its political and humanitarian effects, including unemployment, hunger, an unprecedented migrant worker crisis, and widespread loss of access to healthcare. These crises could have been averted or lessened with planning, but they are now an essential part of India’s lockdown story. So what might have motivated the hasty decision to lock down?
When a complex and unclear threat is unfolding, you can see the appeal of a lockdown to those in power. It reframes disease control in terms of restrictions on movement and contact. Messy narratives about health infrastructure, testing, tracing, monitoring, probabilities, education, research and so forth, are replaced with a list of rules, responsibilities and consequences. The relationship between authorities and people is simplified: the authorities enforce the rules, the people comply.
Moreover, it is almost self-evident that a sufficiently rigorous lockdown must “work” – if we are willing to ignore all the realities of what such rigorous lockdown would entail. There is now an obvious culprit for any failure to control disease – the lockdown violator – and the narrative of lockdown helps build support for solutions based on surveillance.
A second effect of lockdown – also convenient for the central government – is that it “localises” both disease and politics. Because people, and hence infection, cannot move freely, the epidemic takes very different paths in different regions. Consequently, responses to it need to be local, and the responsibility for day-to-day disease control passes to state governments or municipal corporations.
As the national picture becomes increasingly complicated, the Centre takes on a more symbolic and supervisory role. Dashboards of “performance” in different states shift perceptions of responsibility and place pressure on state governments. It is they who become the villains when there is failure to control the disease, even where national policy may be to blame. The blame game encourages states to manipulate data including finding ways to underreport Covid-19 infections and deaths.
Lockdown and disease spread
Lockdown has two overlapping effects on disease, and modelling suggests that distinguishing between them is important for understanding the situation in India. First, lockdown slows the transmission of the virus by enforcing physical distancing. Second, it slows the geographical spread of disease – the localisation discussed above. Neither effect is absolute, of course. Poor lockdown planning led to panic and overcrowding, probably accelerating transmission for a while. And within some localities, for instance in Mumbai, hindering freedom of movement might actually have accelerated disease by stopping outflows of people which could have reduced population density.
The first lockdown effect, a slowdown in transmission, occurs via a reduction in those day-to-day events which spread disease. This can be modelled simply as a reduction in the probability of infection events occurring. It should manifest in the data after a delay. If transmissions are cut in half today, and testing typically occurs 10 days after infection, then 10 days from now recorded infections will start to slow. About 18 days from now, we will see deaths start to slow.
he second effect – geographical localisation – can be modelled most simply as a reduction in the effective population where disease is able to spread. Of course, localisation can never be perfect: disease leaks – across state boundaries, within a town, or even within an urban neighbourhood. Nevertheless, after lockdown begins, we expect fewer outbreaks in new localities. The outbreaks which fail to occur would take time to become numerically significant – hence, their absence takes time to be visible in data. We see clearly the signature of localisation in the flat data curves of outbreaks which have effectively been controlled such as that in Germany.
We can use data from Maharashtra to demonstrate the difference between the effects of a drop in transmission and disease localisation.