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Young Digital Leaders Malaysia

Movement Analysis Based on Community Mobility Reports

by Najlaa Ramli for Young Digital Leaders

Regulation of Movement

We are all now familiar with the concept of movement restrictions and social distancing. The concepts were foreign to most of us just a few months earlier. COVID-19 outbreak has triggered various disease containment measures, including the abovementioned efforts.

Malaysia panned out Movement Control Order (MCO), effective on May 18, 2020. MCO primarily prohibits mass movements and gatherings across the country. MCO also inflicts the closure of all educational institutions, government, and private premises except those involved in essential services. Movements are limited for access to daily essentials. Numerous roadblock operations by PDRM also curbed the movements of the general public as a reminder for the public to stay at home and abide by the order.

Conditional MCO, or CMCO, was enforced beginning on May 4, 2020. Restrictions are eased out to allow certain business activities to resume operations. Movement restrictions are also lifted, except for interstate travels. At the time of writing this article, CMCO would remain in force until June 9, 2020.

Community Mobility Reports from Google and Apple

We seek to study the actual change of behavior of the general public in response to these policies. MCO compliance reports from relevant authorities are available. Nonetheless, it is worthwhile to explore alternative tools and available data that may help us to execute important decisions.

Leveraging on sets of data on users’ Location History, Google issues the COVID-19 Community Mobility Report that provides insights on how busy certain types of places are. The report demonstrates how the visits and length of stay at different places are changing compared to a baseline. The report includes relevant categories of places such as Grocery & Pharmacy, Retail & Recreation, Workplaces, Transit Stations, Parks, and Residential areas. 

Source: Google COVID-19 Community Mobility Report

Apple also shares its aggregated data on the volume of direction requests on Apple Maps. This data is used as a proxy to gauge the number of users who would be traveling from one point to another destination, thus signaling the potential volume of movements. They compare daily changes of the volume to a baseline volume in January 2020.

Source: Apple Mobility Trends Reports

Behavior during pre-MCO

The general public anticipated an official announcement for a lockdown measure on the night of March 16, 2020. This outlook prompted a large number of people to head over to grocery stores and stock up on the daily essentials. Needless to say, visits to places under Grocery & Pharmacy category recorded a surge. The highest number of users was recorded on March 17, a day before the MCO took effect.  

Source: Google COVID-19 Community Mobility Report

Behavior during MCO

As expected, visitation to public places saw a plunge on the first day of MCO implementation. Mobility trends for places under the Retail & Recreation category, which includes visits to the restaurants, shopping centers, and movie theaters, saw significant reduction. Mobility trends for Workplaces also dropped about 50% compared to the baseline. Alternately, data subjects are observed to occupy Residential areas as they adhered to the MCO’s guidelines.

Nevertheless, outcomes on the first day were not the most optimal and desirable. Confusion and misunderstandings amongst the public on the sudden MCO implementation were expected. The following few days saw more stringent monitoring, including implementation of penalties, to reduce MCO violations. The mobility trends at public places were further suppressed to about 60-80% below the baseline rate by the end of the first week of MCO.

As the ambiguity of the MCO measures cleared, the general public started to adapt their daily activities and movements according to the imposed restrictions. Data has backed this new adaptation, and we can observe stable mobility trends from week 2 of MCO onwards.

Source: Google COVID-19 Community Mobility Report

Behavior during CMCO

CMCO aims to reactivate the suppressed economy and social activities during the MCO. The public is now allowed to move freely, although certain rules and standard operating procedures (SOPs) are still applied. Mobility in all categories, except in Residentials, is depicting an upward trend. Visitation to public places has increased by about 2-9%, in comparison to the scenes during the MCO. This analysis is not taking into account visitation to Grocery & Pharmacy, which is considered as access to essentials.

Source: Google COVID-19 Community Mobility Report

Mobility Changes during CMCO vs. MCO

Day TypeGrocery & PharmacyRetail & RecreationWorkplacesTransit StationsParks

Source: YDL’s analysis of Google COVID-19 Community Mobility Report, comparing the median value of mobility changes during CMCO and MCO


Before the COVID-19 crisis, the analysis of users’ Location History data was primarily to identify when a local business tends to be the most crowded. Meanwhile, data on direction requests is a mere data product for transactions in Apple Maps. However, these types of data provide useful observation on the responses to public policies. Public adherence to policies that aim to slow down the rate of transmission is observable during this period of the pandemic. Also, these data provide insights on how the general public restarting their business-as-usuals, given that various alternatives to achieve productivity have emerged during MCO. Ultimately, these types of data enrich our understanding of the changes in behavior as we are adapting to the new circumstance.


  1. Google COVID-19 Community Mobility Report (
  2. Apple Mobility Trends Reports (

Covid-19 Government Response Tracker: A Dynamic Comparative Reflection of Global Performance

by Dr Dhesi Baha Raja, Dr Adlan Suhaimi & Khairul Omar for Young Digital Leaders

1. Preamble

Wide-ranging responses have been launched by countries around the world since the onset of the Covid-19 pandemic in their bid to minimize its toll within the shortest time possible. The enemy is unseen and its true nature unknown, with some governments either throwing all resources possible on one end of the spectrum, while others opt a more liberal approach by minimally restraining and relying upon good public literacy and trusting self-regulatory social measures.

Benchmarking governmental responses which may vary in timing and stringency as exemplified by the Oxford Covid-19 Government Response Tracker (OxCGRT) attempts to provide insights on the impacts following various state interventions. Towards the end, we shall visit various state interventions in the betterment of our understanding of how we have fared in relation to others.

2. Performa of Different Countries

Our comparative analysis goes further three-fold by combining disease trends of respective nations, testing and their stringency index in our attempt to provide greater width and depth of the effectiveness of respective policies implemented.

Data for the number of confirmed cases and deaths in the following sections is obtained from John Hopkins Coronavirus Resource Centre while the number of tests performed is sourced from Our World in Data, a collaboration between University of Oxford Martin Programme on Global Development and the non-profit organization Global Change Data Lab.

2.1 South Korea: government policies vs. cases

The number of daily cases was successfully brought to a stable base-line after an initial peak. Reciprocating was a steady increase in the number of fatalities as expected with a steady decline. South Korea had a consistent Testing Policy (TP) complemented by an automated Contact Tracing Policy (CTP), both of which decisively instituted early and uninformedly. Apart from School Closure (SC) and Public Event Closure (PEC), South Korea eluded implementing stricter social measures via lock-downs. Arguably, both policies of CTP and TP were pivotal in their success without resorting to stifling public movement nor to socio-economic activities.

2.2 Sweden: government policies vs. cases

Sweden had from the onset opted for a sustainable policy, one which aims to attain Herd Immunity (HI) without overwhelming their healthcare services nor compromising the economy to much effect. Community suppression was avoided apart from school closure (kindergarten through grade nine were open). As with South Korea, businesses and restaurants remained opened. Social distancing was self-imposed where many abided. Public literacy of the latter being an asset to their success as was the case in South Korea. Tests were minimal as were other stringent social measures. Both the number of cases and deaths (reaching 3000 cases as of early May) have steadily declined. With a population of 10 million, Sweden leads other Nordic nations in the number of deaths. Their policy of waiting for natural immunity to develop remains, which is largely supported by the nation.

2.3 Singapore: government policies vs. cases

Singapore had extensive tests conducted from the onset. Similarly, as with South Korea, automated CT played an important role in containing the outbreak at the initial phase, though compounded of late by local transmissions and by the foreign worker clusters. This is reflected by the late peaks of both cases and deaths of recent. Noteworthy is their early decision to practice wide-spread testing and stringent automated CTP allowing early detection and decisive informed intervention.

2.4 United Kingdom: government policies vs. cases

Initially opting for HI as with Sweden, the UK made a late decision to impose stringent policies via lock-down and testing were ramped up in tandem. Clearly indecisiveness had resulted in the escalation of cases and with reciprocal deaths. The UK continues to struggle with healthcare facilities being overwhelmed with the economy tail-gaiting in the aftermath.

2.5 Malaysia: government policies vs. cases

Strict public control measures were implemented in three phases from the onset. The number of official cases and deaths peaked gradually attaining an almost plateau-like crescendo with a decline following. Tests were incrementally instituted over time. Manual Contact Tracing is an on-going exercise in limiting the spread of the epidemic. The government’s recent policy in gradually retracting the Movement Control Order (MCO) and its subsequent results will be much anticipated over time both on effects on health and on the social-economic front.

2.6 Ranking of Stringency Index: Malaysia vs other countries

Malaysia was ranked at 68.4 as of 7 May 2020 on the Stringency Index ahead of developed nations such as Sweden and Austria. The total number of cases and deaths have been kept minimal throughout these measures.

3. Discussion

Various measures have been instituted by different states in their efforts to contain the disease with different results. The outcome decided from the onset being crucial on the course of action being decided by different countries. Arguably, many nations had opted for stringent measures as an immediate effect to prevent disease transmission and to reduce mortality. Some had decided for sustainability and had bitten-the-bullet to achieve Heard Immunity through community exposure at the expense of higher morbidity and mortality.

The role of Automated Contact Tracing has emerged as an important tool for early informed intervention, which is a hallmark of efficient outbreak management. Countries such as South Korea, Singapore and Taiwan have demonstrated that technology can effectively assist in outbreak management.

Big Data should be an essential tool of the future in this respect. It allows rapid data integration and analysis at the disposal of policy makers to execute important decisions. The dynamics of newly emerging diseases not only makes available valuable information for clinicians but also to policy makers and researches alike.

In this respect, issues pertaining to greater data transparency, data confidentiality and data sharing is invaluable. The time has come to adopt a pragmatic approach towards data sharing especially in addressing global pandemics where the speed of communicating information affords early and pre-emptive action to be taken which, ultimately could save millions of lives.

Extensive testing with rapid Lab Turn Around Time (LTAT) has also shown to be pivotal in arresting chains of disease transmissions when implemented timely. Timeliness in attaining real-time-data as opposed to ‘lag-time’ data does not only allow one to assess the disease burden but also to allow early interventions. Dynamics of newly emerging diseases can also be analysed with the availability of timely data. Ultimately, test results represent the most important variable which determines policy making.

The success of less stringent policies undertaken appear to be possible where communities had a high literacy on self-imposed social restrictions where, public compliance was crucial. It allowed society to function with almost normality despite certain social restrictions. Socio-economic activities transpired with minimal stifling.

Various policies have been adopted by Malaysia with variable degrees of stringencies. Guided by the disease burden, the outcome has been favourable to date. The decision to gradually lift social constraints was made to balance both health and non-health determinants. The next course of weeks will decide how effective these decisions are in sailing through the uncharted waters of the Covid-19 pandemic.

4. Conclusion

Benchmarking stringencies as a performa of favourable policies can serve as an important indicator in our quest to seek the best solutions. There is no ‘Magic Silver Bullet’ policy to date for comparisons or benchmarking to learn from. It should serve as a tool for continuous improvement on a journey to the finishing line of ending this pandemic.

Estimating Economic Impacts Due to Impaired Workforce Productivity (PART 1)

by Najlaa Ramli for Young Digital Leaders

Updates of MCO in Malaysia

Movement Control Order (MCO) in Malaysia has been exercised since March 18, 2020. The order requires civilians to show solidarity by enforcing social distancing for the safety of all. On the flip side, this measure has deprived business-as-usual for many economic and social activities. This measure has also impacted the optimal productivity of the current workforce in most economic sectors. However, Malaysia is starting to lift some restrictions allowing a large number of economic activities to restart on May 4, 2020.

The Prime Minister conceded that the country records economic losses for each day the MCO is being enforced. Up until now, the country has recorded losses amounting to RM63 billion, or approximately RM2.4 billion each day. Taking the complexity of the economic environment into consideration, we are exploring a quantitative evaluation to understand the interplay between workforce productivity and the extent of the economic losses. 

Model for Epidemic-induced Workforce Losses

Santos. et. al developed a dynamic model capable of generating sector-disaggregated economic losses based on different magnitudes of workforce disruptions. The model was used as an instrument to analyse post-2009’s H1N1 pandemic. It is an extension of the Inoperability Input-Output Model (IIM) developed by Jiang and Haimes (2001).  

The model by Santos. et al. took into consideration the rate of absenteeism and working hours lost during the pandemic. These were used as measures of workforce disruptions. Meanwhile, economic losses give information on the monetary value to the impairment to the overall production of goods and services. The complete framework of their model is presented below:

Source: Santos (2012) Risk-Based Input Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sector

In the period of social distancing, non-essential services experience sharp declines due to decreased demand. On the supply side, productivity is impaired because of restricted business operations and labor supply. It creates productivity impairment to the economic sectors to produce at its maximum potential. Due to inter-sectoral linkages, adverse impact in one economic sector cascades into other sectors, whether they are directly or indirectly related to the initial perturbation.

YDL’s Inoperability Input-Output Model

Therefore, we at Young Digital Leaders (YDL) are commencing our attempt to work on a simple Inoperability Input-Output Model, which combines the inoperability level of the sectors and economic impact assessment. The dependence of economic sectors on workforce productivity directly impacts the inoperability level. Meanwhile, the input-output (I/O) framework offers a neat way to describe cascading economic effects and the economic system’s sensitivity through inter-sectoral linkages. This model is based on preceding models.

Sample output:

The I/O framework

Department of Statistics Malaysia (DOSM) published the Supply and Use Tables that are essential in the construct of this I/O framework. We are also leveraging on Gross Domestic Product (GDP) and employment data points from DOSM in this model.

Our model currently estimates the impacts for two measures – Output and Value Added in the Malaysian economy. Output is the broadest measure of economic activity that considers total gross value in production. Meanwhile, Value Added refers to the additional value over the cost of inputs used in production (i.e. the difference between revenues and expenses on intermediate inputs). Value added is regarded as a more meaningful measure of economic impact as it avoids double-counting during each round of impacts. 

The I/O framework allows us to estimate total economic impact through three different categories – direct, indirect, and induced impacts. Direct impacts result from changes associated with the specified sector – in our case, impairments to full sectoral productivity. Indirect, or second-round impacts, result from impacts to the suppliers of goods and services to the specified sector. Induced impacts result from the displaced income of workers in direct and indirect sectors, in impacting further rounds of household spending.

Next steps

After the establishment of a framework for this model, we will continue to perform our analysis with data points related to impaired sectoral productivity in Malaysia. As mentioned earlier, we hope this model will be helpful to illustrate cascading effects of certain changes that are taking place in our economic environment. Especially with the recent restrictions lift, it is important to understand the trade-offs that we have been and continue to experience in regard to the economy.


  1. Santos (2012) Risk-Based Input Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sector. National Center for Biotechnology Information
  2. Santos JR. Inoperability Input-Output Modelling of Disruptions to Interdependent Economic Systems. Systems Engineering. 2006;9(1):20–34
  3. Haimes YY, Jiang P.  (2001) Leontief-Based Model of Risk in Complex Interconnected Infrastructures. Journal of Infrastructure Systems. 2001;7(1):1–12
  4. Congressional Budget Office, U.S. Congress (2006)A potential Influenza Pandemic:Possible Macroeconomic Effects and Policy Issues

Evaluating the Reliability of the Number of Confirmed Covid-19 Cases Reported by Various Countries

by Khairul Omar for Young Digital Leaders

Working assumptions

It can be hard to gauge the true extent of the spread of Covid-19 outbreak in each country since different countries have different resources and policies in conducting tests. In comparison, the number of deaths caused by the virus are more likely to be reported correctly as seriously ill individuals would seek medical attention, plus more rigorous reporting procedures in recording such cases.

Under-reported official positive cases from around the world can be caused by missing cases related to positive individuals who are asymptomatic and those who recovered at home without being tested. Low official figures can also be attributed to delays in reporting due to poor laboratory capabilities and other logistics issues.

Evaluation methodology

Data for the number of Covid-19 deaths were gathered from John Hopkins University while the number of tests were derived from Our World in Data database. In both cases, data is compiled using official government sources and press releases.

By plotting the number of deaths vs. the number of confirmed cases and the number of tests performed, we ran linear regression models to find the relationship between the measures in question. Countries that lie close to or above the regression line can be interpreted as being more likely to have adequate testing and reporting policies when comparing to other countries with similar severity, as measured in the number of deaths. On the contrary, a given country can be interpreted as being more likely to have inadequate testing and reporting policies if it lies further away below the regression line.

We wish to emphasise that while this method may not be a guaranteed way in assessing the reliability of official figures by country, it could provide a guide in benchmarking a given country’s policies versus other comparable countries. Please exercise caution in interpreting the results as there may be other underlying factors at play that may not considered in the model.

Are reported cases under-estimated?

In the analysing the number deaths versus the number of official positive cases, the area shaded in orange can be interpreted as countries where the official number of confirmed cases are more likely to be under-reported due to various reasons. Based on this method, we may have reasons to believe that the number of total cases reported for Malaysia so far can be used to represent the actual situation on the ground, especially when comparing with other countries with almost similar number of deaths such as Hungary and Ukraine.

Are countries testing enough?

We can also benchmark if Malaysia is testing enough by looking at how other countries are performing. Similarly, as in the analysis above, we shall use the number of deaths to gauge the spread of the virus in the population instead of using the official confirmed cases figures. Using a regression line to find a relationship between the two, we can interpret that countries that lie below the line in the shaded area are more likely to be under-testing its population. South Korea and Germany, which have been praised by the WHO for their testing policies, are well above the regression line.

The Clarifying Role of Epidemiology – How to Interpret Demographic Visualisations

The above graph has been making it’s rounds on the internet over the last week. It highlights the distribution of COVID-19 cases in Malaysia.  As simplistic as they seem, graphs such as these can inform the ascertainment of high-risk populations, and direct control activities such as screening of populations. All visualisations have caveats, and the plot above is no different. Epidemiological intuition in interpretation of such graphs can provide us some insight into the bad, the nit-picky and the good of such visuals. We explore these themes below.

The Bad

First and foremost, the issue with the above bar-plot is that it reports case counts in identifying groups that are most numerous in testing positive. In epidemiology (and common-sense), frequency of any state should be presented as a ratio, using a denominator common to all comparator groups. In epidemiology, one such metric is the incidence ratio- which is the fraction of new cases (in a particular group) over the total population. This would provide us with the true ratio of positive cases in a particular group of people. As can be seen below, the incidence ratio gives a far clearly picture of test postive numbers by age groups. This picture is also far more consistent with what we know (as of know) with regards to patterns of symptoms by age groups- as clinical manifestation appear more in older groups  (Mizumoto, Kagaya, Zarebski, & Chowell, 2020).This in turn appears to  directly mediate the yield of positive screening test numbers (Wang et al., 2020). Benchmarking on age-related data from other similarly affected coutries will likely provide a clearer picture of this .

The Nit-Picky

Interval classes. The choice of interval classes, is heavily subjective- and is generally left to the best sense of the data analyst, epidemiologist, statistician, scientist etc. However, the use of ‘non-standard’ age groups makes the development of composite ratios, such as the incidence rate above, very challenging. Age-group data from public datasets such as the census can be rendered useless as the comparison of mid-year populations in these public datasets use a standard interval of “0-4,5-9,10-14..etc” whilst the above uses a interval class of “1-5, 6-10, 11-15…etc”. The difference may not look like much, but it makes any further use difficult- and renders graphs such as the one above a mere approximation that is susceptible to error.

The Good

However, despite the critique, there are important reasons why the graph did highlight something very important. This is illustrated in the graph below: a comparison of population counts by age group and positive tests by age group.

Here we visualise something very interesting, the population distribution (in pink) grants clues regarding populations with particularly low rates of positive tests. There are two possibilities that can exist within this hidden population:

  1. They have not been tested.
  2. They have tested negative.

The implications of either possibility are important. If this population has not been tested, then the low-cumulative positive test rates observed (~3% over the last 4 days, average remains ~7%), is a signal for the surveillance system to increase its sensitivity in screening. One possible action is for the screening machinery to focus on individuals within these age-groups instead.

The implications of the second though are more complicated. High rates of negative tests would mean high rates of false negatives- which in turn would signal (perhaps) the need to reformulate the current screening protocol- which of course is not an easy task considering the lack of scalable alternatives currently. This of course also highlight the issue of false negatives- which have been informally reported to be as high as 30% (Lanese, 2020). However, with no systematic analysis into the use of the rt-PCR [1] as screening- we maybe for the present moment- incapable of detecting this “hidden population”.

It is important to note that the trail of data ended at one simple graph, with no connectors. It is therefore near impossible to extrapolate possible improvements that can be made via such limited data. The take home here is succinct – In this war we fight together, never has open data been more relevant than today.


Lanese, N. (2020). Even if you test negative for COVID-19, assume you have it, experts say | Live Science. LiveScience.

Mizumoto, K., Kagaya, K., Zarebski, A., & Chowell, G. (2020). Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance, 25(10), 2000180.

Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020). Detection of SARS-CoV-2 in Different Types of Clinical Specimens. JAMA – Journal of the American Medical Association.

[1] Reverse Transcriptase Polymerase Chain Reaction

Benchmarking government response on Covid-19 in Malaysia vs. key countries

by Khairul Omar for Young Digital Leaders

As Malaysia heads into its third week of Movement Restriction Order (MCO), we seek to study how the lockdown is impacting the number of confirmed Covid-19 cases and to compare various performance measures with other countries. University of Oxford is currently the leading authority in tracking government responses from around the world in handling the crisis under its OxCGRT data repository. Based on multiple responsive measures such as movement restrictions, contact tracing and testing policy, OxCGRT generates a stringency index as a measure to rank countries on how strict the government policies are, which is updated daily.

Tables above is a summary of the latest scoring for each response category for Malaysia and other countries by OxCGRT, with the stringency index between 0 to 100 displayed next to the country name. Malaysian government responses are currently ranked at the lower end of the upper tier with an index of 71.0, although there are more improvements to be carried out particularly in ramping up tests and formulating a more effective contact tracing procedure. The latter is particularly important when the time comes for MCO to be eased or lifted in order to avoid a second outbreak, which is currently carried out by South Korea to keep its level of new cases at a relatively low and fairly constant level after the initial peak.

As new government measures are introduced, the stringency index is updated to reflect the latest ranking on how each country handles the crisis. The two charts below, each for comparing Malaysia with Asia-Pacific countries and the West, are useful in analysing how different governments reacted, particularly in identifying if there have been lost opportunities that could have been implemented earlier and how countries can learn from each other ahead of time.

Using data from John Hopkins University Coronavirus Resource Centre, we shall explore how the number of confirmed cases has changed since the lockdown by comparing the situation in Malaysia with other key countries in the Asia-Pacific region and in the West. As most countries in the analysis have completed nearly three weeks of lockdown, we are currently in a good position to conclude that most if not all countries are a seeing a drop in the exponential growth of the total confirmed cases thanks to social distancing which reduces the basic reproductive number (R0), namely the number of people that each positive person can infect. Looking at the situation in Malaysia, if the growth rate at the start of the lockdown remained unchanged, it would lead to the doubling of the number of total cases in 4 days compared to around 2 weeks after three weeks into the lockdown.

Looking at the impact on the growth factor alone does not paint the full picture into the severity of the crisis. Despite all the signs that total cases are growing at a slower rate, countries that started off with a very high number of cases continue to see a large numbers of daily new cases due to the exponential nature of the pandemic which would take more time to taper off into much lower levels. The chart below shows how the number of new cases has changed in relative to situation at the start of lockdown (set at an index of 100) which is useful in comparing countries where the number of cases is several orders of magnitude different. Note that only countries that have gone through at least a week of lockdown are shown the analysis below.

Austria and Switzerland may well have had their worst behind them and are already planning on easing some of the toughest restrictions in the coming weeks. Italy and Spain are also showing signs that recovery is under way after three weeks of lockdown, but it is not entirely clear if the same could be concluded for the United Kingdom and Canada just yet.

The trend in the number of new cases in Malaysia is still inconclusive for now as it appears that there is no clear sign yet that the level is going down or worsening, as it now hovers around 165 new cases mark for the past two weeks. It is hoped that the ongoing measures will bring the number of new cases further down before plans for easing the restrictions could be considered.

An overview of global fiscal responses to COVID-19 pandemic

by Najlaa Ramli for Young Digital Leaders


Earlier in March 2020, International Monetary Fund (IMF) has recommended priority areas in which the government must uphold in the time of COVID-19 pandemic. The mentioned areas are:

  1. Prevention of people from contracting the disease, containment & suppression of the disease, and treatment to those who are affected;
  2. Provide timely, targeted and temporary cash flow relief to those who are most vulnerable/affected in the time of crisis; and
  3. Prepare a protection and continuity plan for individuals and businesses during the crisis and easing out afterward.

The priority of every government is to protect the wellbeing of its people. In facing the serious global pandemic crisis, governments around the world are reacting to limit the human and economic impacts of the COVID-19 outbreak. Here is an overview of the magnitude of fiscal measures that have been announced by some governments so far:

Picture 1 Source: IMF, government announcements of stimulus packages

Following the surge of positive cases of COVID-19 recorded worldwide, governments started announcing their intervention measures. Whilst containment and suppression measures via social distancing and lockdown are deployed, fiscal policies or government’s spending and tax instruments are being expanded.

Taking the amount of fiscal stimulus as a percentage of a country’s GDP, Malaysia seems at the forefront at over 15%. Prihatin Economic Stimulus Package (ESP) announced on 27 March 2020, altogether with an economic stimulus package for vulnerable sectors announced earlier in late February, fiscal stimulus package in Malaysia amounted to RM250 billion (~US$58 billion).

However, it is too premature to claim the interventions taken by our country are either enough, or effective. Other governments are also reacting to the crisis actively by continuously reviewing and preparing for additional stimulus as the situation needed. For example, Singapore initially announced its fiscal package to deal with economic slowdown on 18 February 2020. A supplementary budget was announced on 26 March 2020 that includes the expansion of subsidies and cash payouts to households. United States of America (USA) also has announced its stimulus package in three phases since early March.

Healthcare expenditure: A Priority during public health crisis

COVID-19 pandemic is first and foremost a public health crisis. Hence, the priority of government responses should lie on health care systems to contain and suppress the outbreak. In the absence of medical countermeasure such as vaccines, the alternative approach involves rigorous testing, treatments for all infected patients, readiness of intensive care units, and adequate support to healthcare workers.

In general, all healthcare systems are undergoing capacity constraints during the outbreak. The existing number of hospital beds and intensive care units will not be sufficient with the spread of the outbreak. The stockpile of medical gear, such as ventilators, medicines, and personal protective equipment (PPE), are depleting. Additionally, support and protection for frontline healthcare staff are essential in tackling the outbreak.

Picture 2Source: IMF, government announcements of stimulus packages

Hong Kong allocated about HK$30 billion or 1 percent of its GDP for its newly created Anti-Epidemic Fund. The allocation is to provide additional resources to its Hospital Authority and sufficient protection for frontline healthcare staff. Hong Kong is allocating the fund for the deployment of additional manpower, procurement of additional PPE and other services related to medical, such as for cleaning, security, transportation, storage, clinical waste disposal, and hospital supplies.

Social aid: Cushioning the economic shocks and uncertainties

COVID-19’s containment and suppression measures require some degree of social distancing and movement restrictions of the general public. The trade-off from this measure is curtailed economic activities that introduce shocks to supply and demand in the economy.

It is also a task on hand for the government to cushion the impact of the inevitable economic fallout and ensure proper measures are prepared for the subsequent need to get the economy up and running again. Heightened uncertainties during this crisis lead to a drop in consumption and investment, in which it triggers a chain reaction that worries the household and businesses.

Picture 3Source: IMF, government announcements of stimulus packages

For households, objective of fiscal policy should be focusing on income support, especially during the quarantine or in the time of temporary laid-off. Households who are considered as the more vulnerable group are those dependent on daily wages, who have low income, disabled and homeless people. Many households are also expected to be on temporary unemployment because of the COVID-19 shock. Social assistance ranging from cash transfers, discounts, or unemployment insurance are amongst the temporary support that can be offered by governments to protect their distressed populations from the economic repercussions of COVID-19 crisis. In general, most governments are providing and delivering this assistance for the next three to six months for their resident, with the hope that the pandemic situation will ease out soon.

For businesses, their concern is having adequate cash flows to pay workers and suppliers. Tax relief, subsidies, deferment of loan payment, flexibility in lending to small and medium-sized enterprises and credit guarantees are amongst offer that government may present as options.

Australia has announced several stimulus packages in March 2020. They started with an announcement of wage subsidies for businesses. Subsequent measures include payments to jobseekers and payment for employers to pass on to employees for them to stay in work. Reserve Bank of Australia also announced a three-year funding facility to help banks continue to lend to businesses.

Expect evolving reactions from the governments

Unprecedented interventions by the governments at this junction are needed, without a doubt. Under this COVID-19 circumstance, governments are actively figuring and accommodating to their best to cushion the impact of this health emergency and economic downturn. Enhancements to stimulus packages are being announced as we navigate through this unprecedented crisis. Ambitious interventions to mitigate this episode are important for everyone – young and old, low and high income, individuals and businesses.

The impact of Covid-19 lockdown by key countries

by Khairul Omar for Young Digital Leaders

Benchmarking against China’s success story

China is the only major country that has managed to bring down the number of new cases to very low levels based on official figures. This was achieved by a strict implementation of lockdown that went beyond the level practiced in Europe and elsewhere. Lockdown was only eased 60 days after it was first imposed, at the time when the number of new cases were practically zero. In fact, near-zero level (both in terms of new cases and day-on-day growth) was achieved around 2 weeks prior to the easing of the lockdown, but the strict measures remained intact.

Based on the lessons learned from China, it would be premature for other countries to lift movement restrictions currently in place until the benchmark seen in China has been reached in order to avoid future outbreaks. While the strictest form of lockdown has been lifted in China, many other measures are still in place there until the government is confident that the outbreak is truly behind them.

South Korea: containment without lockdown

South Korea managed to control its outbreak through containment and contract tracing without the need for lockdown measures. While this analysis focuses on the impact on movement restriction, it would be beneficial to study how the outbreak phases panned out in South Korea and what other countries can learn from it.

Also note that unlike China where new cases tend to hover around a single or two-digit, South Korea to date stabilizes at around 100-case mark which it will continue to manage in preventing it from exploding to a new peak. This may well be the outcome for other countries, which further suggests the importance of continuous efforts to contain the spread and not to let our guards down when the worse may appear to be behind us.

Countries currently under movement restrictions

While the United States is the worst-affected country to date, there is still limited timeframe available to analyse its lockdown impact as the country continues to exponentially grow in new cases. Therefore, we shall not be looking at the United States for comparison and focused our benchmark towards 4 key European countries with nationwide restrictions.

Italy, once the worst-hit country behind China during its peak, have started to show positive signs from the lockdown around 3 to 4 weeks after it was first implemented in the north. With the latest day-on-day exponential growth at around 5% and total case of over 115,000 as of Apr 2, the number will still continue to rise in large numbers, but it does seem to be moving in the right direction towards a gradual recovery.

Spain seems to be around one or two weeks behind Italy in terms of the pace of recovery. There appears to be a first sign of a shift in the past few days but it may still be early to conclude that the nationwide State of Alarm is driving total cases down. France is in their third week of lockdown where the first sign of a turn may be on the horizon as in the case of Spain, but more time is needed before an fair assessment can be made.

United Kingdom is relatively behind in imposing a strict movement restriction. After nearly two weeks of implementation, an upward trend in daily cases with an exponential growth of above 13% is still being observed. As in the case of its neighbouring countries, it may take another week or two before the first signs of a positive effect could be observed.

Malaysia is currently in the second week of Movement Control Order (MCO). Although it appears that the number of new cases is heading towards a stabilizing trend instead of going upwards, more time is needed to assess if the restrictions imposed by the government are having a significant impact on the number of new cases. A continuous downward trend and a flattening in new cases needs to be sustained for a substantial amount of time before the movement restrictions can be loosened or lifted.