How to create a coronavirus forecast

CORONAVIRUS PATIENT DEMAND FORECAST
Corona virus COVID-19 patient-based Drug forecast

For creating a forecast, it is best to divide the forecast into 3 buckets. We will take an example for coronavirus forecast.

1. Part 1: Epidemiology – Identifying patient pool

2. Part 2: a) Market- Identifying patients on competitor the product in market

b) New Product patents- Identifying patients that will move to our product in the market

3. Part 3: Conversion – Converting patient numbers into pills and then pills into revenue.

Another important part of the information you should have is-

1. Country of launch: for example if you are launching in Canada. Each province act as a Country due to its regulator and access law. This will also affect the review based on the population in that country and county the healthcare system.

2. Is there a Tender business

3. Are there any parallel import or export form the country

4. Are there any off label use of drugs in your therapy area

5. Is the government encouraging generics or innovation

6. vial split (make sure one drug is being used by one patient)

7. what are other alternative treatment options available to patients which are not pharmacological

Part 1: Epidemiology – Identifying patient pool•

Understand first do you need to create an incidence-based or prevalence based forecast. • Generally, for chronic indications, forecasters make a prevalence based forecast. but if the disease has an indication that has a different phase of treatment for example in the first phase patient needs A treatment and in other phase, patient needs B treatment. You should consider making an incidence-based forecast. In another example of Coronavirus patient forecast, we will create an incidence-based forecast. Because patients get infected then either survive or die the same year. they do not carry disease for long term

• Because in each phase the patient pool will behave differently and will have different treatment duration.

• Now let us assume you want to create an incident-based forecast for an infectious disease eg COVID-19 in which patients have different dosing frequency and drop off or die within a year.

To start, 1. Take all the population in the country from 5 years back of your current year 2. Split the population into the age group or gender that you need for your forecast. for example, if disease effect people with a certain age group 2-11 and 12-60 and 60+ age group. If this disease does not act on the people certain with age do not include them to the calculation pool. 3. Apply incident rate to that population(Or probability of how many can get infected) 4. Apply Diagnosis rate – of all the carriers of the disease might not show symptoms and will not go for diagnosis 6. Treatment rate 7. Apply any other filter specific to disease 8. By now you will have a baseline of the patient pool that is available in the market for the treatment for your and competitor products. By the end of Part 1, you will know how many active coronavirus forecast patients will be there in the market that you can target.

Part 2: Market- a) Identifying competitor product in market • Understand beforehand, to what ATC class your drug belongs. • Because this ATC class will be your market to a competitor for patient share. 1. Start with all therapy classes that exists in for the treatment of the disease. 2. Have the main idea of what percentage of patients are each therapy class. 3. Then identify what percentage of patents are on each product within those therapy classes. 4. This will act as a baseline for your new product Usually this kind of information can be taken data vendors, for example, IQVIA MIDAS data. By the end of Part 2, you will know how many patients are using a competitor product.

b) New Product patents- Identifying patients that will move to our product in the market Keep in mind you should have the following assumption data in your hand before creating a forecast. 1. Launch date 2. Peak share 3. SOB 4. Tmax 5. LOE date 6. Share post-LOE 7. LOE Curve 8. Drug per patient 9. Cost of Drug 10. Reimbursement date 11. Public/ Private split 12. Compliance rate 13. Market expansion Post and pre-event

Part 3: Conversion – Converting patient numbers into pills/injections and then pills/injections into revenue.

Management Ad hoc request that you will have to address Scenarios High, base, low • Share change • New formulation • Competitor launch • Event due to government regulations (Fast drug approval process)• Reimbursement • Modification in the date of launch • Indication extension • LOE • Price elasticity

Dashboard for presenting the realtime patient numbers: https://coronavirus.jhu.edu/map.html