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Using maths to combat COVID-19

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‘The pandemic has now reached a level in which no human can make an optimal decision without the aid of a computer. Therefore, we need to start working on quantitative models to identify optimal decisions, instead of pointing fingers for not making proper decisions, when decision making is literally beyond the capacity of a human.’ – Senior Lecturer, Department of Mathematics, University of Colombo,    

Dr. Anuradha Mahasinghe

by Sajitha Prematunge

What has math got to do with a pandemic? At the outset, it might seem the two are completely unrelated. One has only to observe that the number of infected in certain districts is higher than that of others and making informed decisions based on those numbers could mean the difference between stifling a cluster and a full-blown third wave. Senior Lecturer, Department of Mathematics, University of Colombo, Dr. Anuradha Mahasinghe knows only too well how important the numbers are in combating COVID-19. This is why, in June, he and his colleagues proposed an optimization model, aimed at minimizing the damage to the economy, while confining the COVID-19 incidence to a level endurable by the available healthcare capacity in the country, while their compartment model projected COVID-19 transmission. Their study investigated the effectiveness of the control process with the aid of epidemiological models.

 

Epidemiological models

Mahasinghe explained that an epidemiological model is a model that simulates and describes an epidemic. “Modelling is essential if you want to describe a phenomenon. From the spin of an electron to the rotation of the heavenly bodies, these phenomena are understood with the aid of models. It is the models that help us describe the changes in economies and fall of financial markets,” said Mahasinghe. And pandemics are no exception. He explained that an epidemiological model, based on a reasonable theory and supported by evidence, is a collection of entities and their operations, that when put together simulate and describe an epidemic, which provides important insights into the transmission of the disease.

But how does maths factor comes in when dealing with a pandemic such as COVID-19? Mahasinghe pointed out that math is inevitable whenever dealing with numbers or quantities. “Aren’t we really sensitive to numbers in this COVID era more than ever? Every person is anxious to know the numbers of reported cases and deaths.” One might say the numbers are governing us because decisions are also made based on these numbers. However, these numbers are only the smoke, warns Mahasinghe. “One should be able to make a better decision if he sees the fire. Therefore, to make the best decisions during the pandemic you have to look into a mathematical model that can best describe the phenomenon.”

Numbers may govern us but what governs the numbers? According to Mahasinghe, this can only be uncovered by a model that captures the quantitative aspects of the pandemic. “It’s what we call a mathematical model which provides us with an explanation to the occurrence of these numbers.” Such a model can also forecast how these numbers are going to change in the future.

But how credible are these models? Hopefully, they are nothing like the local weather forecast. “Such models are based upon very fundamental and well-accepted laws in nature such as energy conservation, which cannot be falsified,” reiterated Mahasinghe. “Such models are supported and validated by empirical evidence. People use such models very often to make decisions in industry to make profit. So, why not look into the numbers and the math behind them to make optimal decisions accordingly, in a pandemic scenario?”

 

Where we went wrong

When asked where Sri Lanka went wrong in attempting to contain the pandemic, Mahasinghe said, “I guess we didn’t see the fire, we only saw the smoke. More precisely, we didn’t pay enough attention to the transmission dynamics or to optimal decision making. We were able to make some good decisions in a qualitative sense, but I seriously doubt we had the insight to make quantitatively sound decisions.” He pointed out that even when the decisions were made, the outcome could not be predicted due to a lack of a mechanism to forecast.

When asked whether the authorities were too quick to lift the lockdown, Mahasinghe answered in the negative. “I don’t think it was too early. Lifting strict lockdowns was essential at that moment. We were struggling to achieve two conflicting goals; containing the disease and sustaining the economy. Stepping down from strict curfew to partial lockdowns is indeed a good decision in such a context.” But was it methodical? Was there a mechanism to decide on the nature of the partial lockdowns? Did we know how to optimally restrict mobility in order to achieve those conflicting goals? Did we know to what extent the lockdown of a district should be optimally eased? Did we estimate the potential increase in positive cases from a district when its lockdown would be relaxed? Did we know the magnitude of the economic loss caused by shutting down a region? These are questions Marasinghe believes that authorities should have paid attention to, when easing the lockdown.

“Lockdowns could have been relaxed with the aid of proper optimization models capable of providing answers to these questions.” He repeated that such models are used very often in industrial decision making and provide promising solutions. “You can’t bring the COVID incidence down to zero even with such models, but at least you know what’s going on and the effectiveness of a decision so the health sector can take relevant measures.

According to Mahasinghe, authorities have overlooked the significance of data. “Even now I don’t think enough attention is paid to data.” According to him, some important data were not gathered. For example, he pointed out that, despite Western Province residents being advised against crossing borders, some invariably did, as there were no strict rules against it. “There is no point in regretting the fact, but we could have counted the number of vehicles that crossed the borders and used it to estimate the impact on transmission.”

He explained that the entire country can be regarded as an epidemiological network, where the nodes are the cities and the interconnections are the roads. “There are elegant models in network theory to gain many insights into transmission through such a network.” He also noted another pertinent issue, that even if data were gathered, they were not used. “Much effort was made to gather and organize COVID related data such as incidence per region etc, and that is really commendable. However, have we used them; what were the insights we gained into transmission from them except for some trivial speculations?” questions Mahasinghe. He reiterated that such insights can only be gained through an extensive study that involves the collaboration between mathematicians, computer scientists, epidemiologists and economists. “The mathematician’s part alone includes exhaustive algorithmic development and computational modelling challenges,” explained Mahasinghe.

 

Criteria

When asked what factors were taken into consideration in their optimization model, Mahasinghe reminded that a delicate balance must be struck between two conflicting goals. “We need to find the optimal compromise between containing the disease and sustaining the economy. As a developing country, we can’t afford beyond a certain level of the control process, so budgetary constraints must be considered.” It is obvious that COVID-19 is transmitted through human mobility. He pointed out that, consequently, inter-regional travel plays a significant role. “On the other hand, transmission dynamics can be modelled to a certain extent by well-known compartment models. However, human mobility affects the compartments and the relevant model has to be moderated accordingly to reflect that reality.” The optimization model considers all factors, such as medical capacity to deal with the pandemic, economic concerns, transmission dynamics, regional contribution to the economy, and generates a lockdown relaxation strategy that keeps the level of incidence below a desired threshold, while minimizing damage to the economy.

However, Mahasinghe pointed out that this was a prototype and it can be made closer to reality by incorporating more constraints. “For instance, I haven’t considered the fact that most agricultural activities are done in the North Central province. But, if required, that too can be incorporated without difficulty.” According to him epidemiologists and economists can introduce more constraints to the optimization model, and the applied mathematician’s job is to overcome the computational challenges posed by incorporating them.

 

Relevant?

There is no point in closing the stable doors after the horse has bolted. Months after the lifting of the lockdown are such models even relevant? “The compartment model that captures the transmission of COVID-19 is still applicable, irrespective of any lockdowns, unless it is quite certain that there is absolutely no community transmission. I think we were in such a stage only at the very beginning of the first wave,” said Mahasinghe. According to him, the network-based model that captures human mobility is also applicable irrespective of lockdowns or any other preventive measures. In contrast to these, the optimization model is applicable in its existing form only when lockdowns are in force. “Having said that, this model may still be useful with some changes in the present context where small regions are isolated. For instance, a slightly changed variant of that model can determine which areas should undergo isolation. Moreover, it is possible to modify the optimization model further to be used in the process of making decisions on identifying the persons to be quarantined.”

Human mobility is a critical factor in the spread of a pandemic as well as any models targeted at managing such, how could a mathematical model factor this in? “Not only COVID-19 but even dengue is transmitted mainly due to human mobility. A mosquito doesn’t travel very far during its lifetime. Humans are more responsible for carrying diseases.” Mahasinghe pointed out that COVID-19 is not very different. “If you know the way humans move from place to place, and also know the level of incidence in each place, it is not that difficult to model how the disease is transmitted through humans.” He observed that most preventive measures are also focused on restricting human mobility, which he deemed commendable. “A mathematical model can prescribe the optimal way to restrict mobility.”

What are the implications of mobility? For example are people of certain districts more inclined to travel and therefore may contribute more to the spread of the disease and are such implications reflected in the numbers? “As long as the model is deterministic and you can overcome the computational challenges by necessary algorithm development, closed-form and conclusive solutions can be generated.” Mahasinghe implied that math helps to see the big picture. “Consequences of travel from the Western to other provinces is obvious. However, considering the transport network, Southern and North Western Provinces are also at high risk.” He observed that less attention has been paid to those regions. This begs the question, are the Southern and North Western provinces a time bomb waiting to go kaboom? He reiterated that special attention must be paid to regions that are relatively less danger, such as North Central, which contributes significantly to economic growth, as the Western province is not capable of contributing to the economy in its full capacity. “It is important to keep the incidence at a low level in such places.”

The study predicted that easing lockdown in the Western Province would have adverse repercussions. “As long as vehicles cross inter-provincial barriers, the disease is transmitted to those regions. But in what magnitude? We had access to certain transport data, so we knew to a certain extent how people would mobilise within the country. Also the epidemiological data were available. So we had enough inputs to be fed into our algorithm.” The results were appalling. In fact, this computer experimentation was done in the early days when Sri Lanka was hit by the first wave and there were no strict measures to curtail inter-provincial mobility. During the days in question, Mahasinghe ranked the provinces according to their vulnerability to COVID-19, using another model, by adopting some ideas from network theory. Recently, upon perusing a map that indicated the countrywide spread of the disease Mahasinghe came to realize that the ranking has been validated, eventually. “What I don’t understand is why we failed to foresee this.”

Mahasinghe and his team had access to certain transport data, such as the number of buses, trains and bus routes. However, his models were prototypes. To make the prediction more accurate they would need current transport data, such as the number of private vehicles crossing provincial borders. “There are a number of police barriers between borders, so a vehicle count would not be impossible. If health planners are willing to use that type of model, these could be extremely valuable datasets.”

 

Quantifying the qualitative

In their model they quantify the degree of social distancing. But can criteria so human in nature be quantified? Moreover, how can something as complex as a pandemic, with so many variables, human in nature, be simplified into ones and zeroes? Mahasinghe maintained that it is possible to estimate the degree of social distancing observed, if provided with sufficient data. “I understand that it sounds quite unrealistic. It is because we think of individuals.” Mahasinghe emphasised the importance of noting that they are not modelling an individual, but rather a population. “Though a population consists of individuals, the dynamics of the population is not merely the sum of the dynamics of an individual. When you single out a person, the behaviour of that person is surely very uncertain and unpredictable. Take two persons, they may have certain things in common, so it is not that unpredictable. If you take a thousand people, a lot of commonalities can be extracted and the situation becomes predictable now.” He explained that, therefore, it is possible to assign a value to the degree of distancing with the aid of necessary data.

“Interestingly, it is true that we mathematicians seek certainty in an uncertain world. However, an event that looks uncertain from one point of view looks certain from another.” The toss of a coin is a simple example. “If you toss a coin, the outcome of it being head or tail is widely believed to be uncertain. However, it is the lack of data that makes it uncertain. Suppose the initial speed, the weight, the angle of projection and such were provided, then the outcome may be predictable by basic equations of motion.” Mahasinghe emphasised that math does not guarantee elimination of uncertainty. “That is definitely not the direction the mathematical sciences are moving, specially with the recent developments in quantum physics and unconventional computing. However, where macroscopic events like pandemics or human behaviour are concerned, there are many certainties that we misinterpret under the cover of uncertainty due to our lack of knowledge, eventually missing an opportunity to gain crucial insights into the scenario.”

Mahasinghe pointed out that many decisions are binary in nature. Let alone policy decisions, many behavioural decisions are inherently binary. “For instance, you may decide whether to wear a mask or not. So the one-zero nature of the action is inherent and not artificially imposed by a mathematician.” He further explained that some non-binary decisions can still be quantified. “For instance, if you decide to wear the mask on three days and go unmasked on four days of the week, it can be quantified using numbers and interpreted using probability.” Mahasinghe elaborated that, with recent developments in non deterministic models, applied mathematicians do not hesitate to incorporate uncertainty. “Consequently, uncertainty is no longer immeasurable. It is possible to confine uncertainty of the solution within reasonable limits.

 

What next

With all the talk on vaccination, Mahasinghe emphasised the importance of developing two mathematical models prior to vaccination. The first is a compartment model that explains the post-vaccination dynamics of the disease. “This is pretty standard in mathematical epidemiology. The second, developing a model to capture the effects of the interactions between individuals and predict the outcomes, is subtler and challenging.” He explained that once a phase of vaccination is over, persons in society can be divided into two categories: vaccinated and unvaccinated. “Take a random encounter between two persons. What type of interaction would it be? Is it a vaccinated encountering another vaccinated, an unvaccinated encountering another unvaccinated or a vaccinated encountering an unvaccinated? Obviously, the consequences of these encounters are essentially different.”

The discipline of mathematics referred to as game theory is a promising tool in modelling this type of scenario and forecasting the outcomes. In addition, once vaccination commences, there will be the issue of free riding. Due to different reasons, some people in the high risk category will also choose to remain unvaccinated, eventually resulting in a significant number of potential free riders. Mahasinghe explained that this has already been addressed in the game theory in particular, under evolutionary games. “As a nation we can’t be content with an elementary formula for herd immunity. Instead, we need to develop and upgrade elegant vaccination strategies using compartment models and game theory.” Mahasinghe is of the view that, in this pre-vaccination phase, these two are the immediate concerns that need to be addressed by applied mathematicians.

 

Benefits

When asked what are the drawbacks of not using a mathematical model are and the benefits of using one, Mahasinghe pointed out that in a scenario of conflicting goals and monetary restrictions, it is impossible to make decisions without seeing where the optimal compromise is. “It is easy to put the blame on politicians and other policy makers for not making the right decisions, but how can a human make an optimal decision in this entangled web of parameters, conflicting goals and constraints? Plainly speaking, we need computers to generate the best decisions for us.” That’s indeed what the computers are intended to do primarily, according to Mahasinghe, although they are more frequently used to watch YouTube videos and log into Facebook!

But to perform the intended task using a computer, models and algorithms that can be read by the computer must be created. “That’s why you need to look into optimization, mathematical programming, computational modelling and game theory. This way, you may be able to keep the numbers within certain limits. Also, you can pre-assess a decision quantitatively. Our health workers and armed forces have already committed much and continue to do so and to receive the full benefit of their commitments, the willingness to switch from qualitative to quantitative methods, is essential.

When asked if such models are used successfully in other countries to counter the pandemic, Mahasinghe answered in the affirmative. Since the very beginning, an extensive mathematical modelling process has been done and that’s how the predictions were made. In fact, vaccination models had long been applied to control epidemics even in African countries. In Sri Lanka, there are many misconceptions about mathematical models.” Mahasinghe has observed certain non-mathematicians presenting elementary regressions, numerical approximations and statistical tests, erroneously referring to them as mathematical models.

“Perhaps that’s why some policy makers have lost faith in math. As mentioned earlier, a mathematical model is based on an unfalsifiable conservation law. It cannot be compared to a trivial curve fitting cakewalk. Our people get easily carried away by exotic words. People tend to admire words like machine learning, artificial intelligence and such, but how many are aware of the maths behind these words?” He observed that a closer examination of news reports on machine learning or AI being used in some country to counter the pandemic, would reveal that they are mathematical models and machine learning techniques are used due to the toughness of generating a closed-form solution. “Even to apply computational heuristics, the problem has to be formulated mathematically. Correct problem formulation is a major component of a so-called AI-powered decision.

Mahasinghe explained that the subject of operations research emerged in the new industrial era to enable industrial decision making using computers, as the number of industrial parameters exceeded human ability to process. “The pandemic has now reached this level so that no human can make an optimal decision without the aid of a computer. Therefore, we need to start working on quantitative models to identify optimal decisions, instead of pointing fingers for not making proper decisions, when decision making is literally beyond the capacity of a human.”

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Trump Walks Out of the White House Into A Minefield of Legal Perils!

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WHAT DONALD IS NOW UP AGAINST . . .

by Selvam Canagaratna

“Nobody has a more sacred

obligation to obey the law than those who make the law.”

Jean Anouilh, Antigone, 1942.

“At some point in the next few weeks, Donald Trump will face his second Senate trial following an impeachment by the House of Representatives. Unlike the proceedings in late 2019 and early 2020, this time around — in the wake of the attempted coup on January 6th carried out by a violent mob inspired by Trump’s words to attack the US Congress — the process has been swift,” wrote Sasha Abramsky, a freelance journalist and a part-time lecturer at the University of California at Davis, in Truthout magazine.

The House impeached Donald Trump after a debate that lasted a mere few hours.

Given Trump’s inflammatory words on January 6th, and the unwillingness of senior lawyers to rally to his defense, and given the fact that has now publicly laid blame for the violent events squarely on Trump’s shoulders, the disgraced ex-President’s trial in the Senate could be almost as rapid.

If there is any honour whatsoever among GOP senators — or for that matter, any ability to think long-term about their own political self-interest — he will become the first President in US history to be convicted by that body. Of course, since he will have already left office, he won’t, alas, become the first President to be removed from power via an impeachment and trial process.

That’s a shame, but it doesn’t make the process any less vital. If American democracy is to survive, if political decisions aren’t to be held hostage by gun-wielding fanatics, Trump’s effort to undermine the peaceful transfer of power following an election must face real consequences.

Conventional wisdom has it, however, that most GOP senators, no matter how personally distasteful they find Trump and how terrified they were by his unleashing of a mob against them on January 6th, won’t want to antagonize their base by voting to convict. Conventional wisdom has it that, when push comes to shove, appeasement will win the day.

But in this instance, might conventional wisdom be wrong? As Mitch McConnell seems now to have concluded, and as and many of his caucus likely soon will, having shamefully enabled Trump these past four years, they now have precious little incentive to waste political capital on a wounded and discredited ex-President, a man who has lost his hold on many independents as well as on a significant minority of GOP voters.

To the contrary, they have every incentive, as more and more evidence of his malfeasance surfaces, to utterly disempower this demagogue in order to ensure that he can’t rise from the political ashes to wreak vengeance on those in the GOP who didn’t help him in his coup attempts. Convict him, and they can then, in quick order, pass legislation barring him from ever running for public office again — a fate that, surely, no public figure in American history has so richly deserved, and one that must have McConnell and other GOP leadership figures in the Senate privately salivating in delight. True, this would alienate a not insignificant proportion of the GOP base; but in the long run that might well be less damaging than alienating the independents who are so central to creating a viable electoral coalition for both political parties.

Were the Senate to turn on Trump in this way, McConnell would risk fracturing his base; after all, , and only coup. But if McConnell and the GOP establishment don’t seize this particular bull by its horns they risk being reduced to an extremist party incapable of attracting anyone outside of their shrinking base. In the long run, backing the conviction of Trump might offer them a one-off chance to cauterize their party’s bleeding wound, and to sever its joined-at-the-hip connection to an authoritarian leader who stoked a mob bent on assassinating elected officials. This is a phrase I never thought I’d write, but… “If I were Mitch McConnell, I’d seize the moment and throw Trump as far under the bus as I could possibly manage.”

For here’s the thing: If McConnell doesn’t lend his support — and, by extension, many of the other GOP senators’ support — to conviction, it will only further erode GOP credibility among the broader electorate if, over the coming months, as seems increasingly likely, Trump is indicted in a number of state courts for his myriad crimes. The lower Trump’s legal fortunes sink, the worse the senate will look if it twice exonerated him for his actions despite a preponderance of evidence indicating his guilt.

How would voters react if McConnell, after acknowledging Trump’s culpability for triggering the attempted coup, then pushed to give the man a free pass for it, only to have Georgia show more spine by indicting him for threatening a public official and demanding votes “be found” to guarantee Trump a victory he hadn’t legitimately won?

How would they react if New York State indicted Trump and miscellaneous family members for tax fraud, or campaign finance law violations, or possibly even money laundering, if some of the allegations surrounding his relationship to Russian mobsters turn out to have substance? How would they react if the for his role in the events of January 6? How would they react if — essentially for pimping out his services to foreign governments and entities?

when he leaves office on Wednesday. But, in addition, he is facing a number of as well, including from women who allege he assaulted them in the years before he became President. Given the events of the past two weeks, he may well also face numerous other civil lawsuits, including damages claims from family members of the victims of the January 6 Capitol breach. In each of these trials, evidence will be presented — and the public will see and read that evidence — that will make Trump look more awful by the minute. The further out we get from the Trump era, chances are, the more clear the harm he inflicted will become.

Trump’s corporate backers realize this. Belatedly, he is being cut off from his go-to financing sources, including Deutsche Bank, which has said it will no longer do business with him. As a result, as his legal woes mount, he will likely have to resort to crowd-sourced, dodgy money-making schemes simply to get his gullible supporters to pony up cash to fund his defense attorneys.

Although the fates may have finally caught up with this grifter, the political firestorm he helped create remains. For as Trump leaves the White House, his far-right supporters won’t magically disappear. Trumpism and its toxic spin-offs — from QAnon to the Proud Boys — will remain a threat on the American political landscape for years to come. That, alas, is the sobering reality as a new presidency gets underway and as Donald Trump, from domestic exile in Mar-a-Lago, prepares for his second Senate trial.

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Jagan in R. K. Narayan’s “Vendor of Sweets”

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The world- renowned author R.K Narayan’s novel “Vendor of Sweets” is undoubtedly a worthy contribution to the world of English literature. Born in Madras in 1906, Narayan hailed from an entirely orthodox family. This traditional up-bringing may have influenced him in presenting Jagan’s character in the story.

The story set in the post-independent era in India revolves round, as the title suggests, a vendor of sweets. Narrated in the medium of a third person Narayan uses the English language very effectively to portray characters which are essentially Indian. Yet the reader’s response is rather intimate as the characters transcend time, culture, geographical boundaries, religion etc. thereby achieving universality. In the ensuing analysis let us see how Narayan sketches Jagan’s character to achieve this universality.

As the story begins, we meet Jagan, the vendor of sweets in conversation with his cousin whom the narrator says that no explanation could be given as to how he came to be called so.

The first glance at Jagan gives an insight into his character when he says “conquer taste and you will have conquered the self” This extract from the Holy Scriptures quoted by Jagan was questioned by the cousin, “Why conquer self?” Jagan’s reply was “I do not know, but all our sages advise us so.” This is Jagan who Narayan portrays. The lack of analytical sense is him made him what he was.

This trait in him develops further as the story wends its way towards that tragic end. His limited capacity into in-depth thinking prompted him to accept whatever the sages say. He is unable to give an explanation as to why the taste should be conquered. He accepts it merely because the sages say so. This feature in him prevented his independent thinking. The cousin’s character in contrast with his inquiring mind sheds light on the portrayal of Jagan’s.

We see this trait extending further in his life in most of his dealings. For example, we know that he was in the forefront of the Indian Independence struggle ardently following Gandhi in his nonviolence campaign. What is striking is the fact that he followed Gandhi’s nonviolence policies to the letter and went to the extent of making his shoes out of the skin of an animal which had died due to old age. His words quite rightly justify the point. “I do not like to think that a living creature should have its throat cut for the comfort of my feet”

It is this behavior that makes us think of him as an extremist. He ventures into extremes without being realistic. His attitudes towards his wife’s sickness is one such instance where he became tenacious in the belief that only indigenous medicine can cure her headache. The narration stands to show that their first clash cropped up over such an argument.

The absence of an analytical mind drove him towards diffidence. He lagged behind taking decisions of his own. Even in the transactions with his son he needed cousin’s help to communicate. When his son told him that he wanted to give up his studies in College, he was aghast. His expectations of his son were entirely different. He wanted his son to pursue his studies and collect a BA degree. But he lacked confidence to discuss the matter with the son. He sought cousin’s help to mediate with the son. The cousin’s advice was that it would be best to know from the boy himself. He even suggested “why don’t you have a talk with him?” Jagan responded “Why don’t you?” This is a clear indication of Jagan’s character as a man who is not strong enough to take up challenges.

The home environment was such that the communication between father and son had come almost to a stand-still in the aftermath of the mother’s death. Jagan played the maternal role of feeding the boy properly but he paid little or no attention to the boy’s mental well-being. He was proud that Mali had grown physically. The narration stands to show that he was very proud of his son’s height, weight and growth. But he neglected the fact that as he grows his needs, requirements and aspirations need to be soothed for the wellbeing of his mental growth. He forgot the fact that his son is growing up without the warmth of the mother.

Jagan was in the habit of reading the “Bhagavad Gita” even in the midst of his business activities. However, his concentration on the religious scriptures was invariably hindered with the slightest quietening of the sizzling in the kitchen or if he noticed any slackness at the front stall. If a beggar is spotted by him near the entrance, he would shout “Captain, that beggar should not be seen here except on Fridays. This is not a charity house.” Such acts of Jagan revealed in no uncertain terms his hypocrisy and we know that his hypocritical demeanour was seen in many of his dealings.

Besides, Jagan was somewhat displeased when the trays in the sweet shop returned with the left-overs. It bothered him as if he had a splinter in his skull. When the head cook suggested that they can be turned into a new sweet for the next day, forgetting all his holy scriptures he readily agreed to it, saying “After all everything consists of rice, flour, sugar and flavours…..” His lofty ideals were mere lip-service and clear manifestation of hypocrisy in Jagan.

His hypocrisy does not end at this point. It further extends. We know that he maintained two books to record his business accounts. Narayan, very sarcastically records this act of Jagan when he puts it, “…… arising out of itself and entitled to survive without reference to any tax.” Such acts of dishonesty clashed with his so-called religious principles and the reader responds with discreet sarcasm.

A character sketch of Jagan is incomplete if no mention is made about his inter-personal skills. As mentioned above, his relationship with his wife and son ended in failure and so was his relationship with the members of the extended family. The narration reveals Jagan reflecting “They never liked me” and further the narrator’s words “Thus he had escaped the marriages of his nieces, the birthdays of his brother’s successive children and several funerals” What we gather from the narration is that Jagan felt grateful for being an outcast as it relieved him from his family obligations. This feature in Jagan drives home the point that Jagan was a failure in maintaining inter-personal skills which ultimately made his life pathetic.

This is Jagan we meet in Narayan’s “Vendor of Sweets” In Jagan we see a man not put into a frame. A blend of good and bad. A person made of flesh and blood and we begin to wonder whether we have not met him somewhere, in our daily transactions. Jagan is a victim not of evil but a victim of his own silly, weak or strange but harmless aspects of character. Jagan is essentially Indian but his hopes, aspirations and dreams are universal.

 

Written by Vivette Ginige Silva

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R.K. Lionel Karunasena, fine athlete and exemplary police officer

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Twenty years ago Lionel Karunasena had a heart attack while taking his constitutional walk at the Bambalapitiya Police park and collapsed.

He was born on January 2, 1945, in Ratnapura. He studied at the Seevali Maha Vidyalaya, Ratnapura, excelling not only in his studies but also in athletics. His forte was long jump and the triple jump. He was spotted by the talent scouts of the Ceylon Track and Field Club (CT &FC) and enrolled him to the club and found employment at Air Ceylon.

On November 11, 1964 at the CT& FC- University Athletics dual meet, he equaled the national long jump record of 24 feet two and a half inches established by N.A Weeratunga of the Mercantile AAA on the December 28, 1956.

The writer was a witness of this event. In his allotted six attempts, he jumped over 22 ft. One jump was nearly 25 feet but he over stepped the board. In his fourth jump he leapt into fame equaling the Ceylon record. This record was broken only in 1985!

At the Ceylon 1964 AAA nationals, he was placed third in the long jumps event. He won the event in 1965 and 66. His ambition in life was to serve as a protector of law and order. In order to achieve this, he joined the police as a sub inspector on June 26, 1967.

Despite his busy schedule as a police officer he continued to be involved in athletics representing the police. In 1977, he came third at the AAA Nationals when two Indian athletes, P. Bannerjee and Mohinder Singh took first and second places.

He represented Sri Lanka at the Asian Games in 1966 at Bangkok and again at Bangkok in 1970.

In the all-time list computed by the Sri Lanka AAA recorder, Lionel Karunasena ranks second.

He always believed in equality and denounced social injustices. Due to his dedication towards duty he won quick promotions and rose to the rank of DIG. His first appointment as DIG was to the Wanni. Here he was required to be in the war front. There he was a shining example to his colleagues.

He often visited the many camps in the war zone.

He served as the Commanding officer of the Police STF for over 13 years and was the fourth commanding officer of the STF. He had a miraculous escape when President Premadasa was killed by a suicide bomber on May 1, 1993. Seventeen others were killed along with the President.

He was a highly respected office in the police. His wife Chitra, daughter Sarika and son Shalike were well aware that he was a committed officer and at the same time a loving wife and devoted father. His long and dedicated service will be written in gold. May his journey through samsara be short and peaceful.

 

K.L.F.Wijedasa

100,Barnes Place – 7 Colombo

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