A cluster of results inside a 1000 vote range sounds suspicious, and political parties often frame such patterns as fraud. But statistics tells us whether such clustering is actually unusual or simply what we should expect given the underlying data and normal variations.

The real question is: What is the probability that in 243 constituencies, several winning candidates fall inside the same 1000-vote band (e.g., 100K–101K)?

Intuition: We know that in state elections in a state like Bihar, average voting in a constituency is ~200K votes. In a constituency, what are the chances of a winning candidate getting votes  in the range 100K to 101K? Imagine we know this probability somehow. Say p% chances of this happening, then the problem becomes a simple binomial distribution problem for us to get a sense of it.

Binomial Distribution Refresher:

For the uninitiated, The Binomial Distribution is used to calculate the probability of achieving a specific number of successes (k) in a fixed number of trials (n), where each trial has the same probability of success (p).

P ( X=k ) = nCk pk . (1-p)n-k

(Ignore the formula if this looks unfamiliar, and follow the argument)

So, imagine a tennis player has a 10% chance (p=0.1) of hitting an ace on any given serve. What are the chances he wins the game immediately by serving 4 consecutive aces (i.e., k=4 successes in n=4 trials)?

Answer = ₄C₄ . (0.1)^4  . (1-0.1)^(4-4) =  0.0001 (or 1 in 10000 chances in a game)

How do we estimate p in this case:

Back to the question at hand: How likely was it for 8 candidates to have winning votes in such a tight range (within 1000 votes from each other).

To answer, let’s analyze actual data for total number of votes that were polled in each constituency, as well as % of votes garnered by winning candidates in each constituency. As expected, these are broadly normal bell shaped distributions.

Nothing abnormal till here.

Below are stats for both functions (rounded to nearest logical digits):

Number of Votes:

Mean = 206500  

Std Dev = 23000

Winning Vote %:

Mean = 0.48      

Std Dev = 0.05  

Now, we will use these theoretical normal curves. Assume that Total Votes Polled and % of votes polled for the winning candidate are two independent events (a reasonable assumption) to calculate theoretical distribution of total votes for the winning candidate.

I used Monte Carlo simulation with 1 million runs to calculate this distribution. (each simulation is a random number taken from 1^st distribution to show total number of votes polled in a constituency, and a random winning vote % taken from the 2^nd distribution. Multiple of these two numbers is the number of votes received by the winning candidate)

If you are interested in code or want to play around, feel free to visit it at : https://github.com/bhanu-sisodia/StateElections/blob/main/Election_Winning_Votes_prob.ipynb

Below is the resultant theoretical distribution of winning votes by the candidate in Bihar assembly (based on Total votes & winning vote % distributions)

The peak of this distribution is ~97K votes. The distribution is a narrow bell shape, which means a lot of winning votes are likely to be concentrated around this peak.

Let’s make a simplification to allow us do a quick binomial distribution check:

Let’s take ~25 buckets in and around this peak (12 on each side). While the data is normal shaped, with area under each bucket being ~2% of sides and ~2.7% at peak, let’s take it to be universal in this narrow band. The data range we are interested in is ~85K to 110 K. Total area under this range (highlighted in red color) is ~60% (or in other words, there is a 60% chance that the winning candidate will get votes between 85K to 110K), or 2.4% per bucket.

[Note: We are being conservative by going with this universal distribution assumption, as we see from the chart, the peak is at ~2.7% and is very close to the 100K votes mark. In such scenarios, any simplifying assumption that takes a more conservative route is a reasonable one and wouldn’t distort our conclusion as you will see later]

This 2.4 % is our p value. This is saying that there is about 2.4% chance that the winning candidate will get votes in a given 1000 vote bucket in this range.

The question we are asking is: Given 243 assembly results, how many results can fall under these buckets. A classical binomial problem now.

Results of Binomial distribution:

So what are the chances that either 1, or 2.. up to 8 candidates (cumulative 1 to 7) win within a given 1000 vote range? That’s 76.9%. What are the chances that more than 7 candidate will end up in a bucket like this? That’s ~23.1% (complementary of previous probability). But we have 25 such buckets, what are the chances that at least 1 of these 25 buckets get more than 7 candidates? That’s ~100% (99.86% to be precise).

The probability that at least one of the 1000-vote bucket contains ≥8 winning candidates is ~99.86%

What’s being highlighted as a fishy scam is actually a mathematical certainty!

Though to complete the story, the data had 11 winning candidates that polled between 100K and 101K votes. (8 from winning alliance and 3 from opposition alliance). What are the chances of 11 or more candidates winning in any of these 25 buckets? That’s still a very healthy 58%!!

(This aligns closely with our Monte Carlo model expectations, further confirming that the pattern is statistically normal.)

Final Words:

Isn’t it amazing how the results that look very interesting at first glance come out as not so interesting when we dig deeper. We, as corporate workers and as data professionals, face these challenges very often. The story most of the time originates from our biases (things that should be great, things that should not be working etc), and we often cherry pick data points to feed our own data bias. How come the same team won the best innovator award for last 3 consecutive years? How come 4 out of 10 folks of a team got promoted last year while the function number was only 11 out of 50, several such questions are thrown at us every week. Many of these questions can be tested against binomial lenses.

Binomial distribution is a strong tool to do quick back of the envelop calculations to figure out if things are within domain of being “as usual” or if they are asking for a deeper study?

The post The “100K Khel”: Unpacking the Voting Fraud Claim using Statistics (and Intuition) appeared first on Bhanu Sisodia.

1. Is it the average on-hand Inventory that we should carry?
2. Is it the minimum stock we should hold?
3. Or the theoretical minimum?

The misunderstanding often stems from the fact that different companies have very different Inventory management practices and often use different terminologies.

This is a brief post to dispel some of the more common mistakes we make while dealing with this topic. For the benefit of wider audience and newer supply chain professionals, let us go through some of the basics first. If you are well versed with the topic, feel free to jump directly to part 2:

Understanding Safety Stock:

Imagine a supply chain without any Demand or Supply Variation. We sell (exactly) 100 units/day. We have an exact lead time of 5 days to get new inventory.

If you have 800 units this morning, will you order anything today?

You won’t.. ordering new inventory will make sense only when you are left with 500 units. To generalize, in this case you need to order just when your Inventory position (Net on-hand + on-order) falls to the demand over lead time.

In this case, the net on-hand will go to zero just when new supplies become available. So there will not be any stockout or sales loss.

Above situation simplifies and doesn’t protect against two obvious possibilities:

1. What if demand is higher than forecast? (Demand variability)
2. What if supply takes longer than the assumed lead time? (Supply Variability)

 That’s where Safety Stock comes in picture.

Safety Stock provides a supply chain with the desired buffer against Demand and Supply Lead Times variabilities.

Think of the same scenario, but with demand and supply variability. As you can imagine, in this situation, having an inventory position at just about 500 units is risky. By the time fresh order arrives, there is a chance of us running out of stock. (if the actual demand exceeds 100 units/day)

To safeguard, we need a buffer, above picture shows the same supply chain with a safety stock of 200 units. As you can see, while Inventory may dip below safety stock buffer, it’s likely to remain above zero, safeguarding us against stockouts. In this setup, just before new replenishments arrive (the lowest on-hand position) actual stock will sometime be lower than the safety stock line (during periods of higher demand or longer lead times) and sometime higher – but on average, the on-hand stock’s minimum position should hover around that safety stock line.

Now let’s define safety stock:

Safety Stock is the average level of the net stock just before a replenishment arrives.

Key words here is “Average”. As mentioned above, On hand Stock may breach safety stock or may get replenishment before it even hits Safety Stock depending on variability.

Part 2: Common misconceptions about Safety Stocks –

Misconception1: Safety Stock (SS) is a minimum level that you should never touch.

Reality: SS is an insurance buffer designed to be consumed against uncertainty. In a healthy system, inventory will naturally dip below the SS level for part of the replenishment cycle in some cases(Mathematically speaking ~50% of replenishment cycles are likely to end with OnHand stock being lower than Safety Stock on the day when new supplies connect). In fact, If SS is never consumed, it is likely excessive or planning team is over-responding beyond the set parameters (this is true irrespective of targeted Service levels)

Actionable Takeaways: Don’t trigger a panic or RCA just because On Hand < SS.

Misconception2: Use a fixed, simple rule for all items (e.g., 30 days of coverage)

Safety Stock must be calculated dynamically based on three factors: Demand Variability, Lead Time Variability, and the desired Service Level. A high-value, highly variable “A” item needs a different calculation than a predictable “C” item.

Actionable Takeaways: Stop using fixed days of supply across all items. Embrace probabilistic methods (like using the normal distribution, Z-scores and demand variability in terms of Std deviation or RMSE) to set inventory targets that truly align with business risk and cost. These are rather simple calculations with low sensitivity around their inputs, that means even if inputs are not perfect, output will still be reasonably good.

Misconception3: Safety Stock is calculated once and remains static for months or years

Safety Stock is a dynamic variable that needs frequent recalculation. Key inputs like Demand levels, Demand Variability, Lead Times, Lead time variability change over time and hence Safety stock levels that were calculated several months ago may no longer be valid.

Actionable Takeaway: Implement a regular review cycle (monthly/quarterly) to recalculate SS. A planning software can help in more frequent refresh of safety stock levels.

Misconception4: Safety Stock Fixes Bad Forecasting or can cover other systemic issues

Safety Stock only covers random forecast error (noise), not systematic bias (signal). Most companies fail to apply proper Safety Stock formula to count for a known, persistent bias (more on that in a separate post someday) and usually end up using regular Safety Stock formula on a bias inflated (or deflated) forecasting data. Having a bias, and not handling it properly will usually lead us to higher Safety stock. Inflating SS to cover a consistently high or low forecast bias is expensive and masks the real problem—a flawed forecasting model or process. Similiarly having a wrong BOM, or inaccurate lead time are kind of errors that can’t be hidden/covered by safety stock in most cases. Having high safety stock can mask them but this masking will often lead to far mor serious issues in future (as systemic errors are not uncovered in time)

Actionable Takeaway: Before blindly increasing SS, address the root cause of large errors.

Misconception5: Looking at Safety Stock in Silo as the only controllable factor to ensure Service levels.

Safety Stock is one of several levers for ensuring Supply Chain agility. Other levers involved are: holding Finished Goods (FG) vs. Component/Raw Material inventory, Supplier Dual-Sourcing, Strategic Capacity Planning, or shortening the Order Lead Times. These other levers are oftenmore cost-effective ways to improve resilience than just simply increasing SS at finished goods end.

Actionable Takeaway: Safety stock is the last line of defense. This buffer will cover for demand and supply variabilities as seen in past or predicted for future, but it doesn’t cover for gaps in supply chain design, and if often very costly to maintain. Invest in improving upstream stability (supplier performance, lead time reduction) to ultimately reduce the need for excessive safety stock.

If you are a supply chain professional, I’d love to hear about your views – What other misconception have you seen around safety stocks.

The post Safety Stock Demystified: A Practical Guide For Planners appeared first on Bhanu Sisodia.

In a large temple, devotees from neighboring villages gathered each day to offer prayers. It had a grand idol, a knowledgeable priest, great bhajan singers, beautiful gardens and fragrant flowers, filling the temple premise with a mesmerizing aroma. Every evening, the temple would be lit beautifully, and the flames of the oil lamps would glow, creating an atmosphere of spiritual serenity.
But no one ever paid attention to the man who came in quietly each afternoon to fill the lamps with oil. He would come before the crowds, clean all lamps, refill them, check the wicks and silently merge with the other devotees before the prayers start.

It all went smooth, till one day…

Years rolled by, his work unseen –
A quiet, faithful daily routine.
But one dark day, he fell unwell, and couldn’t be there.
There was no oil in lamps and nobody cared.
The lamps that once gave golden glow,
Began to sputter, dim, and slow.
The wicks burned thin,
The light gave up, the dark crept in.
The priest was flustered, chants went dry,
The children wailed, the mood ran high.

Only that day a realization occurred, the silent lamp-oiler, whom no one ever noticed, was essential to the experience they all took for granted.

That’s also the story of most support functions – especially Supply Chain.
Its often unnoticed, precisely because they work so well.
Products show up “as expected,” launches go live on time, damages stay low – all taken as business as usual.
But what’s rarely acknowledged is how these outcomes are delivered, often amidst volatile demand, constrained supplies, shifting priorities & timelines, and relentless pressure.
Behind the apparent smoothness lies a grueling engine:
meticulous demand forecasting, capacity alignments done months or years in anticipation, detailed supply planning, countless follow-ups, master-data & parameters management, and yes, a fair share of last-minute firefighting.

Yesterday was International Supply Chain Professional Day.
Here’s to the countless professionals who navigate chaos behind the scenes to keep everything running.

They are the invisible oilmen in the temple of modern business.

The post To the Invisible Oilmen of Modern Business – Happy International Supply Chain Professionals Day! appeared first on Bhanu Sisodia.

At first glance, this looks disappointing. But what if I further told you that right after India’s independence, from the 1948 London Olympics to the 1964 Tokyo Olympics, the country consistently ranked in the top 30? Does this mean that India is performing worse now than in the 1950s?

Let’s dig deeper. The 1990s were arguably a tough period for India at the Olympics. In both the 1996 Atlanta and 2000 Sydney Games, India finished in 71st place as well. So, is the 2024 performance comparable to that of the late 1990s and early 2000s?

While these data points are factual, they can be misleading. The problem lies in how we interpret the rankings. The traditional Olympic medal ranking system prioritizes gold over silver and bronze, which can skew perceptions. A country with just one gold medal can rank higher than a country with several silver and bronze medals. But is this the best way to judge a country’s overall performance at the Olympics?

Many would argue that the total medal count is a far better indicator of a country’s sporting prowess. In the 1948-1964 era, India’s position in the top 30 was largely due to its dominance in men’s hockey, where a gold medal was almost a given. Beyond that, India had little presence in other sports, except for K.D. Jadhav’s bronze in wrestling in 1952.

The comparison to 1996 and 2000 is even more striking. In both years, India won just one bronze medal. Because all countries with a single bronze share the same ranking, the difference between one bronze and, say, one silver and five bronze medals (a total of six) is minimal. However, we know that this difference is quite significant—at least six times more.

So, if total medal count is a better metric, how is India doing at Paris 2024? Quite well, actually. With a current tally of six medals, this is our second-best performance ever, only behind the seven medals at Tokyo 2020 and tied with London 2012.

How has India’s total medal count evolved in recent Olympic editions?

This changes the narrative entirely, doesn’t it? When you compare 1996/2000 to recent editions, the country ranking may look similar, but the total medal count clearly shows that these periods are not comparable. The rankings miss the essence of the underlying improvements.

But is the total medal count sufficient? What about near misses? Suppose two countries win five medals each, but one of them also competed (but lost) in five hard-fought semifinals. The country that was consistently in more medal contention likely has a stronger sporting foundation, something not fully captured by just counting medals.

To better assess this, I have added another category to India’s more recent Olympic data since the 1984 Games. This includes events where India either finished fourth (like P.T. Usha’s heart-wrenching near miss in the 400m hurdles finals at the 1984 Olympics) or lost in a match where a win would have guaranteed at least a bronze (such as a boxing quarterfinal, where both losing semifinalists receive bronze, or a bronze medal match in most other sports).

Additionally, not all medals carry the same weight. A medal in a mega-event like soccer or hockey, with thousands of spectators, arguably has more significance than one in, say, sailing or shooting. Moreover, a diverse medal tally, with success across multiple sports, is a better indicator of a country’s overall sporting health.

With these factors in mind, let’s examine India’s Olympic performance since 1984:

Now we see a clearer pattern. India is increasingly becoming competitive in a broader range of sports, winning more medals, and creating more opportunities to win even more. Although Paris 2024 may have seen fewer medal conversions, the Indian contingent has shown that it belongs on the global stage.

Looking at the full picture, it’s evident that Paris 2024 marks India’s strongest showing in its Olympic history, laying a solid foundation for further progress in Los Angeles four years from now.

The chart also reveals some interesting insights: the absolute low point for India at the Olympics was in 1988, when no athlete even came close to winning a medal. The real shift in India’s fortunes began in 2008, roughly 15 years after the economic liberalization and coinciding with the year India joined the $1 trillion economy club.

The post India at the Olympics: Beyond the Medal Tally appeared first on Bhanu Sisodia.

Unlike earlier expectations, vaccines have not magically pushed out Covid and reverted the world to its pre-Mar’20 glory(little joys and luxuries of life that we didn’t value that much at that time.. like meeting near and dear ones, those hugs, office corner gossips, water parks, life without masks.. sigh!), but have they dramatically cut down on infections? or serious illness that needed hospitalizations? or mortality rates?

While the UK is in news on account of having the highest ever cases, but it also provides one of the strongest case (on a big data set of ~70 Million population) in favor of vaccine effectiveness

source/credits: https://ourworldindata.org/covid-vaccinations

The UK has gone very aggressive on the vaccination front with more than 2/3rd of the population being fully vaccinated (~45% of the target population has also taken a booster jab). Effects of this focus and early leads are clear in terms of lower hospitalizations and Covid related deaths.

Russia, on the other hand, seems to be in a mess. These charts are quite inexplicable:

source/credits: https://ourworldindata.org/covid-vaccinations

What’s happening in Russia?

The fully vaccinated population is at ~35%, and while it is about half the ratio vs UK, but is still significant.

And yet, New Covid cases are higher, absolute deaths/day are at the highest, and the most absurd data point is that even mortality rates(3.5%) are higher than pre-vaccination era(2.1%)??

Other than pointing to the fact that sub 50% vaccination levels hardly provide a deterrent for overall society, Only 2 explanations for this:

1. Russia’s early pandemic era data was of bad/questionable quality

2. Sputnik’s efficacy against newer mutations is questionable

What do you think?

Related read: https://bhanusisodia.com/2021/09/despite-a-bad-pandemic-surge-usa-covid-19-data-presents-overwhelming-evidence-in-support-of-vaccines/

The post UK vs Russia: Contrasting tales on vaccination Effectiveness appeared first on Bhanu Sisodia.

Three key games that were played in Squid Games have a clear relevance and learning for Supply Chains. Here is my take on them:

Red light, green light: Anticipate changes and react fast and don’t overdo it just because it is working fine.

Most key Supply Chain decisions will usually come down to one fundamental question: Cost vs Service.

Usually, all Supply Chains would work best when a sweet point on this equation is found and the whole supply chain is geared towards that. However, often, you will find supply chains going overboard with one objective (usually cost). When things are good (music is playing, or say Green Light), things look good. Supply Chain leaders are so pleased with themselves as they could cut costs, reduce redundancy (that looked so .. well.. redundant) and still the supply chain seemingly looks to be doing fine.

Going back to the game analogy, this is when the player is walking towards the finishing line in a carefree manner. This player is pleased with his progress and amused by the fact that other players are taking it so slow.

However, when the music is about to stop (Red Light), very few Supply Chains can re-calibrate. Best example for us is Covid-19 and how it exposed many (proudly)efficient Supply Chains. Supply Chains that went overboard with outsourcing, cost cutting, supplier consolidation, logistics footprint consolidation etc.. all that looked so good on paper till 2019, but then.. one jolt and the value of having a risk hedged supply chain became so obvious. Just like the creepy doll in the game, real world is ruthless with Supply Chains that are caught off guard (too efficient to handle a bit of variability, or too risk hedged to be cost competitive in the usual years).

Honeycomb: Patience is a virtue; else things break down.

Supply Chains are complex systems, and mistakes are costly.

Supply Chains are super complex, you have a sales/distribution network, then one step back, your suppliers also have a similar network, then their supplier, and the list goes on. Any one leg breaking down could seriously impact the whole supply chain.

Hence any changes (separating your shapes from the cookie) have to be dealt in a very delicate manner and after giving it a thorough thought.

Dealing with such complex systems needs system thinking rather than linear thinking. For example, imagine you wanted to reduce costs, one option was to cut usage of Full Truck Loads (FTL) on routes where volume is low and truck fill rates is a challenge. Maybe on paper, using courier service made more sense(say 20% cheaper). So, you change your FTL to courier. But then few weeks later you realize that delays, customer complaints, damages(breakages) & shortages have skyrocketed on such routes. Also, your relation with your FTL partners took a hit and they diverted their best personnel & assets to other account. What went wrong here? Hasty decision without thinking through full system impact.

Or say a hasty move that broke your cookie : )

Tug of War: Coordination is key!

Supply Chains should perhaps be called Supply Maps, as several interconnected processes take place at the same time in the overall system. For a successful supply chain, you need coordination among all key processes. Demand Planning, Supply(Capacity, MTP, STP), Logistics, Manufacturing all must work in tandem to overcome the challenges thrown by the mighty opponent: Uncertainty!

The whole team has to feel the same beat(tension on the rope). For Supply Chains, this beat is provided by the KPIs that are measured and more importantly talked about by the leadership. If all KPIs are compatible and talking to each other, it will mean that all sub-functions will also align their energies on solving the supply chain challenges.

Lastly, a well-coordinated supply chain even with sub optimal sub systems is better than a disjointed best in class implementation of sub-processes.

The post 3 Lessons from Squid Games for Supply Chains: appeared first on Bhanu Sisodia.

Math was rather simple, ~1 Bn eligible adults, 2X vaccination doses, and hence we needed 2 Bn vaccination doses to vaccinate all eligible adults in India.

I pointed out at that time as well that this is an over-reaction.

Why? Because it was not a valid assumption that all eligible adults in India will be reached out or will even be willing to go opt for the vaccinations.  Just like all other nations we were supposed to slow down as we get over administering vaccinations to a section of public that is willing/eager to get vaccinated.

If we look the whole vaccination drive, in India, we have now seen all 4 phases of the cycle:

1. Yes we also started …yawn!: In Q1 2021, Covid seemed like a thing of past, Vaccinations were neither open for general public, nor was there a serious interest/pressure for it.
2. Hey! Where are the Vaccines? When Covid second wave engulfed everyone, within a short span of 4 weeks (reported)cases skyrocketed to 4 times the peak of wave 1, casualties piled up and everyone wanted the vaccination shot to save his or her own life. System crashed, vaccination slots were grabbed up within seconds of opening, it was a nightmare with Supply and administering capacity being a tiny fraction of demand.
3. Supply increased and vaccinations accelerated: In this phase, pace of vaccination drive was still governed by available supply availability.
4. Demand and not supply dictating vaccination pace: How to reach more people? (falling new registrations)

I earlier thought that we will hit phase-4 a lot earlier, but the public, authorities and medical/para-medic Infra surprised everyone.

As explained in another post here, examples from around the world show that beyond a threshold, it becomes very difficult to enroll new people for vaccinations.

For countries like US and UK, they saw the first inflection point once they crossed ~50% of vaccine eligible population. For a country like India we had to overcome so many barriers to achieve high vaccination levels:

1. Education/awareness levels of a developing country
2. Poverty, will people be able to afford this? Will govt be able to afford free vaccinations for public?
3. Rumors! of all kinds and gullibility of general masses to fall for them
4. Geographical spread, far flung and relatively inaccessible areas.
5. Politics: Federal structure with vaccines and Covid in general becoming a political issue
6. A weak medical Infrastructure

And yet, looking back, despite all these challenges, India has done so well on the vaccination front.

For India, we passed that barrier of ~50% vaccination levels without any apparent slowdown in the vaccination pace or demand. We finally hit this inflection point(phase 4) at a time when we have vaccinated ~70% of eligible population in India.

This requires all round appreciation for collective efforts of general public, our pharma industry, medical and para medic staff, and government machinery.

Well done India!

The post Why India won’t hit 2Bn doses mark by December, but still has a lot to be proud off appeared first on Bhanu Sisodia.

The pace of vaccination in last ~6 months have been phenomenal, with about ~3.5 Billion people already vaccinated with at least one jab. However, it will be naïve to extrapolate this to assume that the world will be nearly fully vaccinated in next 6-8 months.

As expected, the vaccination drive faces exponential difficulties in reaching more and more people after addressing the most willing one. Below are examples from 3 developed countries (Source: Our World in Data)

Somehow even in developed countries with a high degree of awareness, education and ease of access, ~50% of eligible population is where the first resistance is met.

This is a point where the (eagerly) ‘waiting to get jabbed’ part of population finishes.. what remains is ‘Not so Sure’ and ‘Vaccine Resistance’ groups. As you see, beyond this most countries are struggling to cover even ~5% additional population per month, and given that vaccines are showing signs of diminished effectiveness post 6 to 10 months of getting first set of shots, our ability to get out of this pandemic by full vaccinating the world seems questionable.

The post Is ‘Vaccinating the Whole World’ a distant dream now? appeared first on Bhanu Sisodia.

Looking closely at the data, USA numbers on face value raises some obvious questions.. with vaccination levels now touching ~55% mark(~65% for people with at least 1 dose), the number of cases and deaths defy basic logic. The peak of recent wave was ~70% as high as the last one seen in early this year, even mortality numbers show a grim picture with recent peak being ~60% of previous peak.

So, are the vaccines really 10X – 20X effective as being claimed in several earlier studies?

Luckily in recent weeks, lot of new data has been published to answer this question more effectively.

To understand why this recent wave is so devastating despite high vaccination levels in population, we need to first acknowledge the presence of more contagious and deadlier variants of the virus.

The Delta variant that contributed ~30% of all cases till mid-Jun now contributes almost all cases.

This new composition of virus variants dramatically affects our susceptibility to new infections and related complications. Hence comparison with previous peaks are to be taken in that light

CDC( Center for Disease Control and Prevention) has come up with a new study in September that further clarifies on significant(though reduced) effectiveness of Covid-19 vaccines vs new infections, hospitalizations and deaths.

Below is a summary of published data:

This data overwhelmingly supports the case for vaccines and even with reduced effectiveness, vaccines reduces the risks by 10+ times when it comes to hospitalization or deaths.

Only caveat is that the study compares the data of Jun20th-Jul17th vs previous base.. while this recent data captures impact of newer variants, but as the cases in USA actually peaked in Aug/early Sept (hopefully), and the fact that Delta variant prevalence in Jun was still lower, maybe the real extent of the dip in vaccine effectiveness is not captured. We need to wait a bit longer to see a similar study with Aug-Sept data.

Or alternatively, we can already see data from other countries and look for patterns.

One interesting case is UK, where with marginally higher vaccination levels(65% fully vaccinated vs 56% in USA) the country was able to cut the mortality by 90%.

What explains this? Different Vaccines? Different mix of variants? Perhaps Yes for both.

But more importantly, above marginal difference in vaccination levels actually mean that there are ~30% less unvaccinated person for the virus to spread in the population.

So I guess pushing fully vaccinated levels beyond ~65% levels is the first step.

If you haven’t taken the jab, please do so.. with the overwhelming evidence pouring from across the world about Vaccine Effectiveness and Safety.

The post Despite a bad pandemic surge, USA Covid-19 data presents overwhelming evidence in support of Vaccines appeared first on Bhanu Sisodia.

Still there are a few realities that need to be told to the new breed of sales trainees. Knowing things in advance is always good so that Jor kaa jhatka dheere se lage. My friends, these are few of the things that you should be prepared for(Apart from traveling in a trolley with goats and life threats from competitors –

The post Promises & Perils of being a trainee in SALES – part 2 appeared first on Bhanu Sisodia.