The GRSI consists of nine sub-indicators, eight on mobility restriction and one on public information campaigns. The distribution of the sub-indicators is shown in Figure 1.
As per Figure 1, it should be seen that at least for some periods, all indicators have reached the highest possible score, indicating that the country has been in full swing in the stringency of the mobility restriction measures. Some indicators, such as school closure, cancelation of public events, and restriction of international travel has been sustained for considerable time. Nevertheless, workplace closing, restriction of gatherings, public transport closing, staying at home requirements, and movement restrictions have been quite short lived. One interesting observation is that public education campaigns, which took a while to come to their fullest, continue at that level even to the end of the study period. The GRSI and Covid-19 New Case Occurrence over the study period for Sri Lanka are shown in Figure 2.
From Figure 2, it is evident that Sri Lanka has witnessed a rapid rise of the GRSI, reaching 100% by 27.03.2020. The GRSI remained at 100% till 17.04.2020, then a slow stepwise decline to 26.85% by 03.10.2020 was observed. However, the GRSI saw a rise to 34.26 over a day and remained so, until 02.11.2020 when it further increased up to 55.09%. With a mild drop to 47.69% only for one day, the GRSI continued to stay at 50% till the end of the observed period.
When comparing the trend of the daily new cases, it is seen that the country was able to contain the first few clusters successfully when the country GRSI was going at 100%. It should also be noted that the large clusters such as the Kandakadu have occurred at times when the SI had been above 50%. With the reduction of daily new cases, it was logical to reduce the level of stringency, as seen in the Figure 2. However, this seems to have changed after the occurrence of a large number of COvid-19 cases all of a sudden. A rise of GRSI of around 8% was seen within a day of the occurrence of Minuwangoda cluster. Within 28 days of the onset of the Minuwangoda cluster, the GRSI stabilized just below 50% of GRSI, giving Sri Lanka GRSI curve a “gutter”. Another striking feature that could be seen is that the large spikes of daily cases being reported, as never before, at times when GRSI.
Having observed the Sri Lankan scenario, he countries in Block 1, those who first contacted the disease were studied.
When looking at China, a striking feature is that despite the large wave of Covid-19 cases in early 2020, the country has been able to maintain quite a flat base up to date. In contrast, China, continued to maintain a GRSI close to 80% up to the end of September. Since then, much lesser GRSI levels with frequent gutters are seen.
Unlike China, South Korea has several large waves of cases, one even continues to rise to date. After rising to a peak above 80% over a couple of months, GRSI dropped to stabilize between 40 – 60%.
The pattern of Sri Lanka with those of countries in Block 2, the members of the South Asian Association for Regional Cooperation (SAARC) region will be compared below.
One characteristic feature observed in the above chart comparing the GRSI of seven SAARC countries was the sharp rise of GRSI observed in March 2020. It should be noted that only India and Sri Lanka scored 100% of the GRSI. India could maintain the GRSI around 80% since it drops from 100%, except towards the gutter observed between September to November. When comparing with the gutter experienced in the curve for Sri Lanka, three such smaller gutters were observed in the curve of Pakistan. Pakistan never reached 100% GRSI, the highest point reached being around 95%. However, except for a drop to around above 60% in May - June, Bangladesh was able to sustain the GRSI at 80% to date, which is the highest recorded GRSI by the time of the final date of reporting. Afghanistan, reached a GRSI of over 80%, which is its highest reached, which was maintained till June 10, however subsequently, a stepwise decline of the GRSI to reach around 10% by the end of the reporting period. Bhutan was the only country in the region that had around 80% GRSI, which only dropped to 65% over the last week of November.
It should be noted that the axis showing the number of Covid-19 cases among the displayed SAARC member states are quite variable. For example, Bhutan has the lowest number of Covid-19 cases reported per day, even the highest not going beyond 40 cases per day. In contrast, India and Pakistan recorded daily cases even going close to 12,000. Thus, the comparisons based on the number of cases between these figures must be done with caution, which is not the intended purpose of these charts. Except for Afghanistan, which has let the stringency fall below 20%, even with continuing case numbers, all other five countries have been able to maintain GRSI around 60% irrespective of the epidemiological trend. This is a worryingly a quite reckless pattern observed in Sri Lanka, which let the GRSI fall below 30% until it was pushed up only up to 50% after a large spike of cases was reported. The countries in Block 3, the more affluent counterparts will be examined in the next section.
Both the USA and UK show that they have been able to maintain around 70 - 80% GRSI values, except in the initial two months. Nevertheless, the case numbers have grown in three waves in the case of the USA and two large waves in the UK.
In contrast, both Australia and New Zealand shows a wave-like pattern in the GRSI values, synchronous with the waves of cases observed. This is clearer for New Zealand than for Australia, which has allowed the GRSI to fall relatively low while revamping it up with the rising caseload.
Considering all the above facts, several vital points could be discussed. Firstly, GRSI could be a versatile tool that the policymakers could use to have an independent insight into the level of stringency of their own decisions (Hale et al. 2020b). In this era of the internet of things and Covid-19 Pandemic, remote monitoring of country policy decisions has become not only feasible and cost effective, but also safe. Since policy makers commenting on their own decisions, as well as their stringency will not only be politically incorrect but also be scientifically unsound, more independent mechanisms such as GRSI could be used for this purpose.
Secondly, the governments need to be aware of the “stringency fatigue” that is observed as gutters, which have been a common feature across many counties. This stringency fatigue could occur as a result of competing interests that the governments may have to respond to. For example, economic impacts of lockdown are potential, much legitimate yet conflicts of interests that governments may have to work with, when looking at solely from a mobility and outbreak spread is considered (Cross et al. 2020). While it is essential to loosen up the restrictions on mobility, this should be based on sound epidemiological evidence. For example, even after recording a massive peak of cases over a short period, when GRSI was well below 30%, the government mechanisms Sri Lank have not been able to raise the GRSI value above 50%. Several arguments are given for this. Firstly, the economic burden of a rise of GRSI. Secondly, the disease is still confined to a localized area, which lead to a lockdown of the affected Western Province. Both are valid arguments (Epidemiology Unit 2020). However, the point is that all possible evidence must be considered by the governments before making critical decisions concerning public health.
Thirdly, a risk-based stringency approach seems to be most suited. The stringency measures must be proportional to the risk. Let’s take the example of Sri Lanka. When the country was managing a few localized clusters, the stringency has been at its peak. However, when the country is facing the largest cluster with blossoming subclusters across the country, it has not been able to increase the stringency beyond 50%. Probably, this is an example of how things should not have been done in a risk-based manner. In contrast, New Zealand appears to be providing a good example of doing things better using a risk-based stringency approach. New Zealand brought down its stringency with decreasing case numbers, however when a large number of cases are reported, they were able to raise the stringency levels back. Subsequently, with dropping case numbers, New Zealanders were able to downscale the measures. This could be one contributor of New Zealand’s success in curtailing the Covid-19 pandemic so far (Jefferies et al. 2020).
There are certain limitations of the current study which needs to be kept in mind. Firstly, the relative importance of sub-indicators which make up the GRSI need to be taken into consideration in a deeper analysis. Secondly, policies related to the mobility are much likely to be influenced by other broader economic and health policies. This interdependence needs to be taken into consideration in future research. Thirdly, the GRSI measures the existence of a policy, not necessarily the adherence and compliance. These latter will vary with mode of implementation, legal measures and culture. Fourthly, the absolute number of cases was used in the current study, however, per capita number of cases would have been a better measure of diseases. For example, comparing India and Bhutan stretches the meaning in talking of absolute number of cases. These limitations need to be overcome in future more detailed work, including using more advanced statistical methodologies, where appropriate.