coronavirus briefing india & the coronavirus: modelling the … · distribution of covid-19 related...

Coronavirus Briefing

India & the Coronavirus:Modelling the Trajectory of Covid-19

Bhramar Mukherjee & the COV-IND-19 Study Group

Webinar, Friday, April 10, 2020, 5:30 p.m. IST

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https://medium.com/@covind_19/predictions-and-role-of-interventions-for-covid-19-outbreak-in-india-52903e2544e6https://medium.com/@covind_19/historic-lockdown-prediction-models-to-study-lockdown-effects-and-the-role-of-data-in-the-crisis-a0afeeec5a6http://covind19.org/

Outline

• Pre-lockdown forecasting

• Post-lockdown analysis

• Epidemiologic models: All are wrong, some are useful

• From numerical forecasting to strategic vision

5

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Data source: Johns Hopkins University CSSECOVID-19 Confirmed New Cases/Recovered/Deaths by Day in India

Couple cases,no intervention

Sudden case increase,screening & travel bans start

Social distancing, closures,more travel bans

Heading towardslockdown

WHO declarespandemic

First case

© COV-IND-19 Study Group

https://www.pharmaceutical-technology.com/news/india-covid-19-coronavirus-updates-status-by-state/

Distribution of COVID-19 related risk factors

7

5

to support the sheer volume of cases. India has the most overstretched healthcare s stem here it is hard to pro ide care in times of normal patient volume. The number of hospital beds per 1000 people in India is only 0.7, compared to 6.5 in France, 11.5 in South Korea, 4.2 in China, 3.4 in Italy, 2.9 in the UK, 2.8 in the USA, and 1.5 in Iran [World Bank]. Need to emphasize that health care workers and front-line workers are among the most vulnerable.

Table 2. Proportion of population in specifically vulnerable subgroups at potentially high risk of COVID-19 severity risk in India

Metric Number†

(in millions) Year Source

Uninsured 1,104 2014 Prinja et al. 2019

Population over 65 92.5 2020 (est.) CIA World Factbook

Hypertension (men)* 175.7 2015/16 Gupta & Ram 2019

Hypertension (women)* 132.6 2015/16 Gupta & Ram 2019

People with cardiovascular disease* 78.7 2016 Prabhakaran et al. 2018

Population with COPD* 75.9 2016 Lancet 2018

Population with asthma* 45.5 2016 Lancet 2018

Develop cancer by age 75 (men)** 70.3 2018 NICPR

Develop cancer by age 75 (men)** 62.3 2018 NICPR

Population with diabetes (adult) 122.8 - IDF

Access to inpatient department facilities*** 731.4 2012 IMS Institute 2013

Access to outpatient department*** 1,104 2012 IMS Institute 2013

† based on 2020 est. of 1.38 billion from UN Department of Economic and Social Affairs * age-standardized; ** risk; *** defined as within 5 kilometer distance of home or work Abbrev.: COPD, chronic obstructive pulmonary disease; IDF, International Diabetes Federation; NICPR, National Institute of Cancer Prevention and Research

3. Do interventions like travel bans and social distancing help arrest the projected exponential growth of COVID-19?

Yes! We took a close look at what might be coming in the next few weeks and months, based on what we have seen in other countries and an improved epidemiological model that have been gainfully employed to

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Forecast after Mar.16

Upper credible limit of predicted cases for India

* indicates any observed value after Mar.16

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India US Italy

COVID!19 Cumulative Confirmed Cases by Day

© COV!IND!19 Study Group

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Lockdown simply buys us time• To ramp up testing, disease surveillance, contact network tracing

• Prepare healthcare infrastructure

• Optimally deploy resources based on emerging hotspots

• Stop the virus: pause and then revive the economy

• Keeping the essential supply chain going and support the vulnerable

• Long-term strategy: instead of discrete short-term tactics 11

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Questions that one can pose

• How many cases do we expect to see post lockdown?

• When can we expect to see a decline in fatalities?

• What does a successful lockdown look like?

• How should we return to normalcy from lockdown?

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Data source: Johns Hopkins University CSSECOVID-19 Confirmed New Cases/Recovered/Deaths by Day in Hubei, China

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Data source: Johns Hopkins University CSSECOVID-19 Confirmed New Cases/Recovered/Deaths by Day in South Korea

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Data source: Johns Hopkins University CSSECOVID-19 Confirmed New Cases/Recovered/Deaths by Day in Italy

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Data source: Johns Hopkins University CSSECOVID-19 Confirmed New Cases/Recovered/Deaths by Day in India

Couple cases,no intervention

Sudden case increase,screening & travel bans start

Social distancing,closures

Heading towardslockdown

Lockdown


Forecasting Models (India specific)

• ICMR• Cambridge• Armed Forces• Sourish Das (CMI)• Ohio State University

18

https://www.ncbi.nlm.nih.gov/pubmed/32202261https://arxiv.org/abs/2003.12055https://www.sciencedirect.com/science/article/pii/S0377123720300605https://arxiv.org/pdf/2004.03147.pdfhttps://www.medrxiv.org/content/10.1101/2020.04.02.20051466v1

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eSIR model: hierarchical formulation and solution

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𝜽𝒕|𝜽𝒕(𝟏, 𝝉 ∼ 𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡(𝜅𝑓(𝜽𝒕(𝟏, 𝛽, 𝛾)).

• Given the values at the previous step, the system can then be solvedfor 𝑓 using approximations.

21

Compartmental Specification

Building autocorrelation

Reff= 𝛽/𝛾

Bayesian specification and Markov chain Monte Carlo (MCMC) sampling

• Priors: 𝜽𝟎~𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡(1 − 𝑌"# − 𝑌"$, 𝑌"#, 𝑌"$), 𝜃%& = 1 − 𝜃%# − 𝜃%$.• Can set priors/initial choices for 𝑅%, 𝛾 in data dependent way.• Begin with large variances that can be tuned with more data

coming in.𝜅, 𝜆#, 𝜆$~𝐺𝑎𝑚𝑚𝑎 2, 0.0001 𝑖𝑖𝑑.

• Learn the SIR dynamics from the observed data first, and then use MCMC to iterate between latent and observed proportions.

22

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Forecasting scenarios and assumptions

• No intervention• Social distancing and travel ban (without lockdown)

Post-lockdown• 21-day lockdown with gradual resumption of activities at

different levels:• Moderate • Cautious • Normal (pre-intervention)

24

R0 under two hypothetical scenarios

25

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quick adherence (1−week delay)

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© COV−IND−19 Study Group


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28

Difference between social distancing andcautious return on May 15: 343,145 cases

Difference between social distancing andcautious return on June 15: 2,376,468 cases

Difference between social distancing andcautious return on July 15: 4,589,917 cases

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as of 07 April 2020Predicted number of COVID−19 cases in India under hypothetical scenarios


Cautious 5/15: 16,520 Cautious 6/15: 251,636 Cautious 7/15: 927,407

Long term – quick adherence

29


Difference between social distancing andcautious return on June 15: 79,492 cases

Difference between social distancing andcautious return on July 15: 65,018 cases

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30


Difference between social distancing andcautious return on June 15: 3,369,834 cases

Difference between social distancing andcautious return on July 15: 6,403,186 cases

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as of 07 April 2020Predicted number of COVID−19 cases in India under hypothetical scenarios



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31


Difference between social distancing andcautious return on June 15: 115,025 cases

Difference between social distancing andcautious return on July 15: 83,560 cases

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a

ScenarioCautious return

Moderate return



as of 07 April, 2020Predicted number of new COVID−19 cases in India under hypothetical scenarios


Cautious 5/15: 699 Cautious 6/15: 12,887 Cautious 7/15: 26,012

7.89Q5;

Takeaway messages• High uncertainty in predicted numbers (note large upper credible limit)

• Variation in predictions increases as we go further in time

• Outputs change everyday and data trumps models

• Relative comparison of interventions are still meaningful

• Need some form of suppression in place post-lockdown (cautious return)

• A rigorous lens to quantify the pulse and movements of an epidemic but an incomplete description of the costs of interventions and what society is undergoing as a whole

33

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14 days

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100,000

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10,000

100,000

1,000,000

0 20 40 60Days since cases reached 100

Cum

ulat

ive n

umbe

r of c

ases

CountryChina

France

Germany

India

Iran

Italy

South Korea

US

Hypotheses• Limited Testing, Testing Strategy and Criteria

• Reporting of deaths

• Temperature

• BCG vaccinations, use of antimalarials, genetics, immunity

• Younger population

35

\

https://www.medrxiv.org/content/10.1101/2020.04.01.20049478v1

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https://fivethirtyeight.com/features/coronavirus-case-counts-are-meaningless/

37

30 March 2020 Imperial College COVID-19 Response Team

DOI: https://doi.org/10.25561/77731 Page 1 of 35

Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries Seth Flaxman*, Swapnil Mishra*, Axel Gandy*, H Juliette T Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunubá, Gina Cuomo-Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Will Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati-Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, Neil M. Ferguson1 and Samir Bhatt*1

Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: [email protected], [email protected]

Summary Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns.

In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death.

One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty.

Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population. The proportion of the population infected

Source: https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-Europe-estimates-and-NPI-impact-30-03-2020.pdf/

https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-Europe-estimates-and-NPI-impact-30-03-2020.pdf/

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https://ourworldindata.org/covid-testinghttp://covid19india.org/

0

20000

40000

60000

80000

100000

120000

140000

3/6/2020

3/7/2020

3/8/2020

3/9/2020

3/10/2020

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US

PositiveIncrease NegativeIncrease

Average positive rate 0.151

Highest positive rate 0.315

US daily tests versus daily incidence

Source: https://ourworldindata.org/covid-testing; covid19india.org


0

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3/11/2020

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3/15/2020

3/17/2020

3/19/2020

3/21/2020

3/23/2020

3/25/2020

3/27/2020

3/29/2020

3/31/2020

4/2/2020

UK


UK daily tests versus daily incidence





0

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1/26/2020

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3/14/2020

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3/20/2020

3/22/2020

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3/30/2020

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South Korea


South Korea daily tests versus daily incidence





0

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3/26/2020

3/27/2020

3/28/2020

3/29/2020

3/30/2020

3/31/2020

4/1/2020

4/2/2020

Iceland


Iceland daily tests versus daily incidence





India daily tests versus daily incidence

0

2000

4000

6000

8000

10000

12000

14000

16000

3/19/2020

3/20/2020

3/21/2020

3/22/2020

3/23/2020

3/24/2020

3/25/2020

3/26-3/27/2020

3/28-4/1/2020

4/2/2020

4/3/2020

4/4/2020

4/5/2020

4/6/2020

4/7/2020

4/8/2020





Summer Boon?

44

Month Correlation 95% Confidence Interval

Countries with non-zero incidence

January -0.185 [-0.55, 0.24] 24

February -0.110 [-0.362, 0.157] 56

March -0.173 [-0.314, -0.026] 175

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https://healthweather.us/%3Fmode=Atypicalhttps://www.google.com/covid19/mobility/

Summary (as data scientists)

• Public health and economics are at a competing interest

• Use of data (mobile network, hospital admissions, temperature maps) to chase the epidemic and nip it in the bud

• Nimble policymaking (pause, push and drive) in a data adaptive way

• Use data to estimate need, deploy resources, how many beds, PPE, ICU beds, ventilators are needed and where?

46

Austrian Poet Rainer Maria Rilke said, “Let everything happen to you. The beauty and the terror. Just keep going. No feeling is final.”

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Back up slides: Length of lockdown

50

R0 under lockdown length scenarios

51


0.0

0.5

1.0

1.5

2.0

Mar 01 Apr 01 May 01 Jun 01 Jul 01Date

Impl

ied

R0

Scenario

21−day lockdown

28−day lockdown

42−day lockdown

56−day lockdown

as of 07 April, 2020




0.0

0.5

1.0

1.5

2.0

Mar 01 Apr 01 May 01 Jun 01 Jul 01Date

Impl

ied

R0

Scenario

21−day lockdown

28−day lockdown

42−day lockdown

56−day lockdown

as of 07 April, 2020



Lockdown lengths – quick adherence

52

0

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Indi

a Scenario21−day lockdown

28−day lockdown

42−day lockdown

56−day lockdown

as of 07 April, 2020Predicted number of COVID−19 infections under varying lockdown lengths


Lockdown lengths – quick adherence

53

0

1

2

3

4

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1Ju

l 01

Aug 0

1

Date

Num

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ses

per 1

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eopl

e in

Indi


28−day lockdown

42−day lockdown

56−day lockdown

as of 07 April, 2020Predicted number of daily COVID−19 infections under varying lockdown lengths


Lockdown lengths – slow adherence

54

0

50

100

150

200

250

May 0

1

Jun 0

1Ju

l 01

Aug 0

1

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Num

ber o

f inf

ecte

d ca

ses

per 1

00,0

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eopl

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Indi


28−day lockdown

42−day lockdown

56−day lockdown

as of 07 April, 2020Predicted number of COVID−19 infections under varying lockdown lengths


Lockdown lengths – slow adherence

55

0

1

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1

Jun 0

1Ju

l 01

Aug 0

1

Date

Num

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ses

per 1

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00 p

eopl

e in

Indi


28−day lockdown

42−day lockdown

56−day lockdown

as of 07 April, 2020Predicted number of daily COVID−19 infections under varying lockdown lengths


A Conceptual Model

56

S E R

A = AUT + ATFN

I = ITTP + ITFP

Unreported, untested cases plus false negative tests

Tested and reported

Managed at home Hospitalized

ICU: severe disease

DeathNon random samplingWho are we testing?

Title slide WebinarNCAER_COVIND19_Slides

coronavirus briefing india & the coronavirus: modelling the … · distribution of covid-19 related...

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