Case Notes/Answers
GD Labs Scaling Swab Testing During COVID-19 By
Bhavin Shah, Arvind Shroff
Discussion Questions:
1. How can Ahuja formulate the allocation problem for swab maximization, without a budget
constraint, for five separate days?
2. If Ahuja chooses weekly (rather than daily) optimization, how will it affect swab
allocation? Which is better: weekly or daily optimization?
3. As outlined by Prasad, the state has a daily testing budget of ₹18 million for the pilot
project. What is the impact of the budget on swab allocations?
4. Besides cost and swab allocation, what other challenges does Ahuja face in planning the
rollout of a swab allocation strategy across the state?
5. What are the managerial implications of the suggested swab allocation plan and its
implementation? How will it help manage future pandemic outbreaks?
, W28362
Teaching Note
GD LABS: SCALING SWAB TESTING DURING COVID-19
Version: 2022-10-17
SYNOPSIS
In early 2020, the rapid spread of COVID-19 across the world led the most highly affected countries, such
as the United States and India, to focus on rapid testing and contact tracing to break the chain of
transmission. By August 25, 2020, India had tested nearly 37 million cumulative COVID-19 samples as
part of the government’s “Test, Track, Treat” initiative (see case Exhibit 1). The efficient allocation of
collected swabs with saliva samples to appropriate testing laboratories (labs) had become an urgent
operational requirement. Naresh Ahuja, the senior consultant of Gangadhar Diagnostics & Laboratories
Pvt. Ltd. (GD Labs), was appointed officer on special duty by the State Government of Chhattisgarh to
devise a plan for the optimal allocation of swabs to government and private labs across the state. More than
13,000 swabs were tested each day across 30 districts in the central Indian state of Chhattisgarh during early
September 2020, with over 100 testing labs operating across the state.
Ahuja decided to roll out a pilot study for two districts that had six labs—both government and private. Shri
Sashi Prasad, the secretary to the Department of Health and Family Welfare, approved the pilot project in
a September 5, 2020, meeting and set a budget of ₹18 million1 for the initiative. That meeting was also
attended by key members of three teams responsible for executing mass testing operations: collection,
logistics, and testing teams (see case Exhibit 5). The task required access to extensive amounts of data on
the collection of swabs with saliva samples, previous backlogs, locations of labs, and the maximum capacity
per lab (see case Exhibits 3, 4, 6, and 7).
The team members who were present at that meeting expressed the following major considerations on the
optimized allocation of swabs with saliva samples:
• A choice had to be made regarding whether to test the swabs in government or private labs, which
differed mainly in terms of cost. The cost of testing swabs in a government lab was ₹800 per swab; the
cost in a private lab was ₹1,600 per swab.
1
₹ = INR = Indian rupee; ₹1 = US$0.0136 on September 5, 2020; all currency amounts are in ₹ unless otherwise specified.
, Page 2 W28362
• Labs in the same district could be overloaded up to a maximum of 100 swabs. Testing each overloaded
swab would cost ₹5,000. Overloading was only allowed in labs within the same district, and the swabs
had to be generated from patients in the same district, to avoid the transportation cost.
• The cost of transporting saliva samples outside the source district was calculated as follows: ₹1,000 per
set of 100 swabs, per kilometre, multiplied by the average distance in kilometres from the district
collection centre (DCC) to the lab outside the district. If a complete set of 100 swabs was not collected,
individual swabs would be transported at a 50 per cent premium above the regular cost (i.e., at a cost
of ₹15 per swab).
• To minimize transportation costs on out-of-district transfers, labs located close to each other were
chosen. Allocation of swabs to any two labs outside of the source district had to be within 20 kilometres.
• The penalty for keeping saliva samples at the DCCs as part of the backlog was ₹6,000 per swab.
A key objective for the team was to minimize out-of-district transfers. The cost for testing swabs at
government labs was half that of testing at private labs, and out-of-district transfers only added extra
transportation costs. Therefore, testing in private labs had to be avoided, especially when the labs were
located outside the district. Labs that were located close to each other and formed a cluster with a radius of
less than 20 kilometres helped minimize transportation costs.
All of these considerations affected cost and logistics constraints for the allocation of swabs (see Exhibit
TN-1A). Ahuja determined that what was needed was the optimal allocation of swabs with saliva samples
from DCCs to testing labs—whether government or private labs, within or outside the district. The goal of
the project was to maximize the allocation of swabs for testing within budget constraints. The pilot project
included both daily and weekly (i.e., five consecutive days) optimization schedules for the comprehensive
rollout of swab testing across the state.
LEARNING OBJECTIVES
The case helps students devise a complex COVID-19 swab testing strategy to ensure timely results, with
the goal of reducing the spread of the virus by identifying affected patients, who can then isolate to avoid
spreading the virus. After completion of this case and assignment questions, students will be able to
accomplish the following objectives:
• Apply linear programming to health care services by meeting the challenges of complex real-life
decision-making for swab allocation to help deal effectively with a crisis such as the COVID-19
pandemic.
• Use the Microsoft Excel Solver function for capacity allocation to understand the mathematical
formulation of a capacity allocation problem by emphasizing cost constraints and solutions.
• Address supply chain resilience to identify the specific factors behind the disruptions caused in the
testing procedures due to the impact of COVID-19 in the health care supply chain.
POSITION IN COURSE
The case is suitable for courses that discuss operations research and health care supply chain management
at undergraduate and graduate levels. It is also suitable for engineering courses that discuss decision
modelling and industrial management and for postgraduate programs in management that cover topics such
as introduction to quantitative decision-making and advanced mathematical modelling. The case also
GD Labs Scaling Swab Testing During COVID-19 By
Bhavin Shah, Arvind Shroff
Discussion Questions:
1. How can Ahuja formulate the allocation problem for swab maximization, without a budget
constraint, for five separate days?
2. If Ahuja chooses weekly (rather than daily) optimization, how will it affect swab
allocation? Which is better: weekly or daily optimization?
3. As outlined by Prasad, the state has a daily testing budget of ₹18 million for the pilot
project. What is the impact of the budget on swab allocations?
4. Besides cost and swab allocation, what other challenges does Ahuja face in planning the
rollout of a swab allocation strategy across the state?
5. What are the managerial implications of the suggested swab allocation plan and its
implementation? How will it help manage future pandemic outbreaks?
, W28362
Teaching Note
GD LABS: SCALING SWAB TESTING DURING COVID-19
Version: 2022-10-17
SYNOPSIS
In early 2020, the rapid spread of COVID-19 across the world led the most highly affected countries, such
as the United States and India, to focus on rapid testing and contact tracing to break the chain of
transmission. By August 25, 2020, India had tested nearly 37 million cumulative COVID-19 samples as
part of the government’s “Test, Track, Treat” initiative (see case Exhibit 1). The efficient allocation of
collected swabs with saliva samples to appropriate testing laboratories (labs) had become an urgent
operational requirement. Naresh Ahuja, the senior consultant of Gangadhar Diagnostics & Laboratories
Pvt. Ltd. (GD Labs), was appointed officer on special duty by the State Government of Chhattisgarh to
devise a plan for the optimal allocation of swabs to government and private labs across the state. More than
13,000 swabs were tested each day across 30 districts in the central Indian state of Chhattisgarh during early
September 2020, with over 100 testing labs operating across the state.
Ahuja decided to roll out a pilot study for two districts that had six labs—both government and private. Shri
Sashi Prasad, the secretary to the Department of Health and Family Welfare, approved the pilot project in
a September 5, 2020, meeting and set a budget of ₹18 million1 for the initiative. That meeting was also
attended by key members of three teams responsible for executing mass testing operations: collection,
logistics, and testing teams (see case Exhibit 5). The task required access to extensive amounts of data on
the collection of swabs with saliva samples, previous backlogs, locations of labs, and the maximum capacity
per lab (see case Exhibits 3, 4, 6, and 7).
The team members who were present at that meeting expressed the following major considerations on the
optimized allocation of swabs with saliva samples:
• A choice had to be made regarding whether to test the swabs in government or private labs, which
differed mainly in terms of cost. The cost of testing swabs in a government lab was ₹800 per swab; the
cost in a private lab was ₹1,600 per swab.
1
₹ = INR = Indian rupee; ₹1 = US$0.0136 on September 5, 2020; all currency amounts are in ₹ unless otherwise specified.
, Page 2 W28362
• Labs in the same district could be overloaded up to a maximum of 100 swabs. Testing each overloaded
swab would cost ₹5,000. Overloading was only allowed in labs within the same district, and the swabs
had to be generated from patients in the same district, to avoid the transportation cost.
• The cost of transporting saliva samples outside the source district was calculated as follows: ₹1,000 per
set of 100 swabs, per kilometre, multiplied by the average distance in kilometres from the district
collection centre (DCC) to the lab outside the district. If a complete set of 100 swabs was not collected,
individual swabs would be transported at a 50 per cent premium above the regular cost (i.e., at a cost
of ₹15 per swab).
• To minimize transportation costs on out-of-district transfers, labs located close to each other were
chosen. Allocation of swabs to any two labs outside of the source district had to be within 20 kilometres.
• The penalty for keeping saliva samples at the DCCs as part of the backlog was ₹6,000 per swab.
A key objective for the team was to minimize out-of-district transfers. The cost for testing swabs at
government labs was half that of testing at private labs, and out-of-district transfers only added extra
transportation costs. Therefore, testing in private labs had to be avoided, especially when the labs were
located outside the district. Labs that were located close to each other and formed a cluster with a radius of
less than 20 kilometres helped minimize transportation costs.
All of these considerations affected cost and logistics constraints for the allocation of swabs (see Exhibit
TN-1A). Ahuja determined that what was needed was the optimal allocation of swabs with saliva samples
from DCCs to testing labs—whether government or private labs, within or outside the district. The goal of
the project was to maximize the allocation of swabs for testing within budget constraints. The pilot project
included both daily and weekly (i.e., five consecutive days) optimization schedules for the comprehensive
rollout of swab testing across the state.
LEARNING OBJECTIVES
The case helps students devise a complex COVID-19 swab testing strategy to ensure timely results, with
the goal of reducing the spread of the virus by identifying affected patients, who can then isolate to avoid
spreading the virus. After completion of this case and assignment questions, students will be able to
accomplish the following objectives:
• Apply linear programming to health care services by meeting the challenges of complex real-life
decision-making for swab allocation to help deal effectively with a crisis such as the COVID-19
pandemic.
• Use the Microsoft Excel Solver function for capacity allocation to understand the mathematical
formulation of a capacity allocation problem by emphasizing cost constraints and solutions.
• Address supply chain resilience to identify the specific factors behind the disruptions caused in the
testing procedures due to the impact of COVID-19 in the health care supply chain.
POSITION IN COURSE
The case is suitable for courses that discuss operations research and health care supply chain management
at undergraduate and graduate levels. It is also suitable for engineering courses that discuss decision
modelling and industrial management and for postgraduate programs in management that cover topics such
as introduction to quantitative decision-making and advanced mathematical modelling. The case also
