Predicting the Impact of Covid-19 in Tamilnadu with ...

Predicting the Impact of Covid-19 in Tamilnadu with Mathematical Modeling

Table of Contents

1. Background................................................................................................................................................3

2. Ameex SEIR-Based COVID-19 Analytics Model.............................................................................4

3. District Wise Analysis............................................................................................................................6

4. Conclusion...............................................................................................................................................15

5. Reference................................................................................................................................................15

Page 02

1. Background

COVID-19 status world over Vs India

The World Health Organization (WHO) had declared the COVID-19 as a worldwide pandemic outbreak on

March 11, 2020. Ever since then, the number of COVID-19 cases have started peaking newer levels on an

everyday basis. World over, it is expected to scale epic heights of over 4 million cases by the first week of

May 2020. Even in countries like India, considered to have, on a large scale, controlled its spread, the

number of active cases is expected to cross newer heights of 50,000 cases.

State of Tamil Nadu scales second largest

In India, the State of Tamil Nadu as on 4th May 2020 stood at the second place only after Maharashtra

adding over 500 cases in a day.

Ameex initiative for Tamil Nadu COVID-19

A gritty analytical approach!!!

In lieu of numerous articles predicting the impact of COVID-19, digital Consultants, and experts at Ameex

have come up with a novel, radical model, predicting the impact of pandemic at granular level. The intent of

Ameex experts is to bring about actionable inferences, that can be consumed by Government authorities in

making informed decisions.

• Experts at Ameex have tried to predict the impact at every district level in Tamil Nadu

• Ameex opines that the idea of releasing lockdown at nationwide or even at state level is not a good idea

considering the uneven impact in different states

• With thorough investigations of everyday data available from Government of Tamil Nadu, Ameex experts

have decided to implement widely used epidemic model called SEIR (Susceptible – Exposed – Infected –

Recovered) at district level

• Further for the study, SEIR model has been implemented for the top Hotspots in Tamil Nadu

Page 03

2. Ameex SEIR-Based COVID-19 Analytics Model

Distinctive, predictive tactic!!!

SEIR is a prevalently used model to calculate the spread of diseases/epidemics among the population. SEIR

highlights the time taken for the disease to reach the peak as well as when it will start subsiding. People in

the population are categorized into:

• Susceptible

• Exposed

• Infectious and

• Removed

Number of cases in each category varies with respect to time. These are predicted using the following

equations.

Basic definitions:

The equation of SEIR model is given as,

dS/dt = -βSI/N

dE/dt = -βSI/N - αE

dI/dt = αE - γ I

dR/dt = γ I

Where,

• S is the number of susceptible individuals who are prone to get infected

• E is the number of individuals that have contracted the disease but are not yet infectious

• I is the number of infected individuals

• R is the number of recovered/dead/resistant individuals

• β is the average transmission rate in population, between the susceptible and infected individuals. This

also shows for how many people does an infected person infects

• α is the inverse of incubation period

• γ is the inverse of mean infectious period

All these variables vary with time. This is the reason why we find equations which are first order derivatives

of the variables with respect to time.

Incubation period (α):

Time interval between initial contact with an infectious agent and appearance of the first sign or symptom of

disease in question. Mean incubation period is identified as around 5 days, according to Americal college of

physicians.

Page 04

Infectious Period (γ):

The time period during which infected people can transmit an infection to any susceptible host or vector

they contact. Mean infectious period is identified as 4 days.

Reproduction number (Ro):Ro is the expected number of cases generated by one case in the population, where all individuals are

susceptible to infection.

By varying this variable, we have modelled lockdowns.

The relationship between reproduction rate, Ro and transmission rate, β is given as:

β= Ro * γ

Approach to arrive at Initial conditions for the model:

• Infectious cases (I) and Removed cases (R) are taken from daily bulletins released by the Tamil Nadu

government

• Exposed cases (E) is calculated using the population density of the district for one Sq. Km around an

infected person

• Susceptible (S) refers to the population apart from E, I and R cases.

Scope

Hotspot Districts as per the higher number of reported infectious cases:

• Chennai

• Coimbatore

• Tirupur

• Dindigul

• Madurai

Assumptions • Part of the total population in an area of interest should be in any one of the four compartments in S-E-I-R

• People who are exposed can either be Asymptomatic and Symptomatic in general. But this is not inbuilt in

the model. People who contract the virus are infected

Page 05

We have implemented the model to forecast in two scenarios,

• Lock Down in Force, considering lower reproduction rate (we will see what reproduction rate is later in

the article)

• No Lockdown, considering higher reproduction rate.

3. District Wise Analysis First day of Analysis: April 29,2020

Chennai District:

Chennai - Model Parameters (Without Lockdown):

Total popultaion Incubation period Infectious period

71,00,000 5 days 4 days

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) a 768

Removed (R)

223

Reproduction number (Ro)

3

Page 06

Inference:

• Without controlling the movement of people, on 7th June,2020 the infected number of individuals in

Chennai would hit the peak value of 9,30,221

• The important fact to note here is, Chennai is a heavily populated district in Tamil Nadu having a

population of 26,903 per Sq.Km. If the condition worsens as indicated in the prediction, the Govt will not be

able to manage the outcome with the existing healthcare infrastructure across city

Chennai - Model Parameters (With Lockdown):

Inference:

• However, with a lockdown scenario, the number of infected cases would peak to 12,196 by 13th May

2020 and then would flatten to 10,000. Hence, for Chennai, the Govt will not be able to release the lock-

down until 15th May 2020. Further investigation would be required pertaining to the situation by this time

for the Govt. to take a call on further lockdown

• At the end of 70 days, i.e., on 7th July 2020, the number of infected people in Chennai would be around

10,804 assuming restricted lockdown in place

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 26,903 768

Recovered (R)

223


1

Page 07

• Removed compartment includes both Recovered, dead and the resistant people who have contracted the

virus but have not shown symptoms even after incubation period because of their good immune system

Coimbatore District:

Inference:

Without controlling the movement of people and, considering the prevailing number of infected individuals

as well as nature of spread of COVID-19, on 16th June 2020, the number of infected individuals in

Coimbatore would hit a peak value of 4,51,593.

Coimbatore - Model Parameters (Without Lockdown):

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 2,928 141

Recovered (R)

121


3


34,58,045 5 days 4 days

Page 08

Coimbatore - Model Parameters (With Lockdown):

Inference:

• On 15th May 2020, the number of infected individuals will reach the peak value of 1360

• The graph subsequently flattens

• Hence for Coimbatore district, it is recommended not to release lockdown by 15th May 2020, so as to

contain the spread of COVID-19 effectively

• At the end of 70 days on 7th July 2020, the number of infected people in Coimbatore would be around

1323

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 2,928 141

Recovered (R)

121


1

Page 09

Tirupur District:

Tirupur - Model Parameters (Without Lockdown):

Inference:

Without controlling the movement of people, on 23rd June 2020 the infected number of individuals in

Tirupur would hit the peak value of 3,23,345.


24,79,052 5 days 4 days

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 478 112

Recovered (R)

91


3

Tirupur - Model Parameters (With Lockdown):

Inference:

• However, with a lockdown scenario in Tirupur district, the number of infected cases would peak to 261 by

17th May 2020

• The number of infected individuals is lesser for Tirupur as compared to above two hotspots, viz., Chennai

and Coimbatore, because the population density is less in Tirupur

• Total population of Tirupur as such is relatively less

• At the end of 70 days on 7th July 2020, the number of infected individuals in Tirupur would be around 260

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 478 112

Recovered (R)

91


1

Dindigul District:

Inference:

• On 18th June 2020, the number of infected individuals in Dindigul would hit a peak value of 2,81,779

• At the end of the 70th day on 07th July 2020, the number of infected persons would be 76,736


21,59,775 5 days 4 days

Dindigul - Model Parameters (Without Lockdown):

Dindigul - Model Parameters (With Lockdown):

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 1,380 80

Recovered (R)

66


3

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 1,380 80

Recovered (R)

66


1

Inference:

• With a lockdown scenario and considering the number of individuals presently exposed and infected in

Dindigul, on 16th May 2020, the peak value of 648 would be reached

• Hence, similar to other hotspot districts, it is not recommended to release the lockdown until 16th May

2020 in Dindigul, for appropriate containment of the virus

• At the end of 70 days on 7th July 2020, the number of infected individuals in Dindigul would be around

634

Madurai District:

Madurai - Model Parameters (Without Lockdown):

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 1624 79

Recovered (R)

40


3


30,38,252 5 days 4 days

Inference:

• Under a without lockdown scenario, on 19th June 2020, the number of infected individuals in Madurai

would hit the peak value of 3,96,560

• At the end of 70 days, on 07th July 2020, number of cases will be 1,22,403

Madurai - Model Parameters (With Lockdown):

Susceptible (S)

Exposed (E)

Infectious (I)

N – (E+I+R) 1624 79

Recovered (R)

40


1

Inference:

• Considering the strict lockdown in Madurai district, the count of infected individuals would peak to 756 on

16th May 2020

• The graph then flattens on the 70th day to 742, on 07th July 2020

• Considering the above analysis, it is recommended that the Govt plan a phase by phase relaxation of the

lockdown after 16th May, based on the investigation performed at that time

• In other words, Govt can plan to resume the labor works by analysis at the level of Taluks

4. Conclusion

• According to the model prediction, in the ‘without lockdown’ scenario:

• The peak values of number of infected individuals would have reached faster

• Number of infected individuals would have been far higher

• The predicted peak infected numbers in these hotspots cannot be handled efficiently considering the

current availability of Isolation Beds and Ventilators in Tamil Nadu

• Hence it is recommended not to remove the lockdown this week. It is better to extend the lockdown for a

minimum of four weeks from May 3rd to have the COVID-19 spread under control

• In other words, the Govt can plan to resume smooth operations, by analysis at Taluk level

5. Reference https://www.ijidonline.com/action/showPdf?pii=S1201-9712%2820%2930117-X

https://stopcorona.tn.gov.in/daily-bulletin/

https://www.worldometers.info/coronavirus/

https://en.wikipedia.org/wiki/Demographics_of_Tamil_Nadu

https://www.elections.tn.gov.in/PDF/DISTRICT-WISE-AGE-COHORT.pdf

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