COVID19 for India Updates
March 24, 2020
IndiaSIM
• We use IndiaSIM, a well-validated agent-based model of the Indian population. This has been published widely over many years and has been used for government decision-making including by NTAGI for vaccine introduction. A brief summary of the model is attached. The model describes the actual Indian population in full detail of demography (age, gender), location, socio-economic characteristics and access to healthcare. Our unit of analysis is an actual simulated Indian and estimates are reliable down to the district level. The model is fitted to the most recently available NFHS and census data and uses state-of-art agent-based modeling methods.
• We fitted the model to available data from China and Italy. Key parameters include force of infection, age- and gender-specific infection rates, severe infection and case-fatality rates. Seasonality was assumed based on the idea that most respiratory infections decline in the summer and although Covid-19’s temperature and humidity is not well understood, it is reasonable to assume that some characteristics of seasonal influenza apply here.
Scenarios
• High – trajectory with current lockdowns but insufficient physical distancing or compliance.
• Medium – Most likely scenario with moderate to full compliance but no change in virulence or temperature/humidity sensitivity.
• Low – Optimistic scenario with decreased virulence and temperature/humidity sensitivity.
What’s behind the state estimates
State level estimates are driven by
• Date of seeding of the epidemic based on available testing data
• Presence of major metro cities where initial transmission is more rapid.
• Flight connections to Covid19 affected countries.
• Age and demographic variables
Predicted Infections from COVID-19 in India
3000
2500
2000
1500
1000
500 0
Total Infections (Asymptomatic + Hospitalized + Symptomatic)
Date Low Me di um
Hi gh
Population (Lakhs)
12/25/19 1/25/20
2/25/20 3/25/20
4/25/20 5/25/20
6/25/20 7/25/20
8/25/20 9/25/20
10/25/20 11/25/20
12/25/20 1/25/21
2/25/21 3/25/21
Predicted Hospitalizations from COVID-19 in India
30 25 20 15 10
5 0
Hospitalized Cases
Date Low Me di um
Hi gh
Population (Lakhs)
1.2.20 8.2.20
15.2.20 22.2.20
29.2.20 7.3.20
14.3.20 21.3.20
28.3.20 4.4.20
11.4.20 18.4.20
25.4.20 2.5.20
9.5.20 16.5.20
23.5.20 30.5.20
6.6.20 13.6.20
20.6.20 27.6.20
4.7.20 11.7.20
18.7.20 25.7.20
1.8.20 8.8.20
15.8.20 22.8.20
29.8.20
UTTAR PRADESH
Total Infections (Asymptomatic + Hospitalized + Symptomatic)
500
400
300
200 Low 100
0
Date Hospitalized Cases
5.0
4.0
3.0
2.0 Low 1.0
Me di um Hi gh
0.0
Me di um Hi gh
Date
1.2.20 15.2.20
25.4.20 9.5.20
29.2.20 14.3.20
23.5.20 6.6.20
28.3.20 11.4.20
20.6.20 4.7.20
1.2.20 15.2.20
25.4.20 9.5.20
18.7.20 1.8.20
29.2.20 14.3.20
23.5.20 6.6.20
15.8.20 29.8.20
28.3.20 11.4.20
20.6.20 4.7.20
18.7.20 1.8.20
15.8.20 29.8.20
Lakhs People
Lakhs People
50 40 30 20 10
0
Low
Me di um Hi gh
DELHI
Total Infections (Asymptomatic + Hospitalized + Symptomatic)
Date Hospitalized Cases
0.5
0.4
0.3
0.2 Low
0.1 0.0
Me di um Hi gh
Date
1.2.20 15.2.20
25.4.20 9.5.20
29.2.20 14.3.20
23.5.20 6.6.20
28.3.20 11.4.20
20.6.20 4.7.20
1.2.20 15.2.20
25.4.20 9.5.20
18.7.20 1.8.20
29.2.20 14.3.20
23.5.20 6.6.20
15.8.20 29.8.20
28.3.20 11.4.20
20.6.20 4.7.20
18.7.20 1.8.20
15.8.20 29.8.20
Lakhs People Lakhs People
100 80 60
KERALA
Total Infections (Asymptomatic + Hospitalized + Symptomatic)
40 Low
20 0
Me di um Hi gh
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
Low
Me di um Hi gh
Date Hospitalized Cases
Date
1.2.20 15.2.20
25.4.20 9.5.20
29.2.20 14.3.20
23.5.20 6.6.20
28.3.20 11.4.20
20.6.20 4.7.20
1.2.20 15.2.20
25.4.20 9.5.20
18.7.20 1.8.20
29.2.20 14.3.20
23.5.20 6.6.20
15.8.20 29.8.20
28.3.20 11.4.20
20.6.20 4.7.20
18.7.20 1.8.20
15.8.20 29.8.20
Lakhs People Lakhs People
350.00 300.00 250.00 200.00 150.00 100.00
50.00 0.00
Low
Me di um Hi gh
3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00
Low
Me di um Hi gh
MAHARASHTRA
Total Infections (Asymptomatic + Hospitalized + Symptomatic)
Date Hospitalized Cases
Date
1.2.20 15.2.20
25.4.20 9.5.20
29.2.20 14.3.20
23.5.20 6.6.20
28.3.20 11.4.20
20.6.20 4.7.20
1.2.20 15.2.20
25.4.20 9.5.20
18.7.20 1.8.20
29.2.20 14.3.20
23.5.20 6.6.20
15.8.20 29.8.20
28.3.20 11.4.20
20.6.20 4.7.20
18.7.20 1.8.20
15.8.20 29.8.20
Lakhs People Lakhs People
100 80 60
TELANGANA
Total Infections (Asymptomatic + Hospitalized + Symptomatic)
40 Low
20 0
Me di um Hi gh
Date Hospitalized Cases
1.0
0.8
0.6
0.4 Low 0.2
0.0
Me di um Hi gh
Date
1.2.20 15.2.20
25.4.20 9.5.20
29.2.20 14.3.20
23.5.20 6.6.20
28.3.20 11.4.20
20.6.20 4.7.20
1.2.20 15.2.20
25.4.20 9.5.20
18.7.20 1.8.20
29.2.20 14.3.20
23.5.20 6.6.20
15.8.20 29.8.20
28.3.20 11.4.20
20.6.20 4.7.20
18.7.20 1.8.20
15.8.20 29.8.20
Lakhs People Lakhs People
Main Takeaways
1. Estimates are based on IndiaSIM, an agent-based model of the Indian population. This model is focused on the period up to July.
2. Immediate social distancing focused on the elderly population is essential. We have modeled a three-week period of complete isolation for the elderly. The longer this period, the more we are able to delay infections into the post-July period.
3. Model is sensitive to hospital outbreaks of COVID19 induced by admission of infected patients into hospitals. Need for large, temporary hospitals to handle this load over the next three-month period. Secondary, hospital-based transmission fuels the epidemic.
4. Testing, particularly of those coming in with respiratory symptoms is essential to separate those in hospitals. Need two-stage, preemptive testing in symptomatic elderly immediately to reduce deaths.
5. Immediate and continuing serological surveys needed to monitor the stage of the epidemic. We are currently flying blind.
6. Ventilator demand will be 1 million. Current availability in India is estimated to be between 30K and 50K ventilators (the US has 160K and is running short in most places).
7. Mortality in healthcare workers could further increase deaths in the general population. Healthcare workers need personal protective equipment (i.e., masks and gowns) to protect themselves. Without them they get sick further straining the capacity of the healthcare system to respond
Summary for Policy
1. Delays in testing are seriously reducing the ability of the population to protect itself. This is the most important way in which we can contain the epidemic. An increase in the official number of detected cases in the short term could encourage the population to take distancing more seriously and will reduce panic compared to a big spike later.
2. Border closures at this stage have little to no impact and add further economic disruption and panic. While international transmission was important in the first stage, domestic transmission is now far more relevant.
3. A national lockdown is not productive and could cause serious economic damage, increase hunger and reduce the population resilience for handling the infection peak. Some states may see transmission increase only after another 2
weeks and lockdowns should be optimized for when they could maximize the effect on the epidemic but minimize economic damage. State level lockdowns in the most affected states could change the trajectory of the epidemic and should commence immediately. Any delay allows for more secondary cases to emerge. Lockdowns should be guided by testing and serological survey data and should be planned on a rolling basis. We will expand these recommendations shortly.
4. Preparedness for case load should be the highest priority at this time. We will be issuing guidance based on the model for state level needs for bed capacity, oxygen flow masks and tanks and ventilators.
5. Temperature and humidity increases should help us in reducing case load. Although the evidence is limited, it is plausible.
6. We need to focus on both children under the age of five and the elderly. Early testing and healthcare in this population could help significantly reduce the mortality toll of the epidemic.
7. We should be prepared for multiple peaks in the model (we have only shown what happens in July) and we should be prepared for more cases and deaths later in the year.
For research, updates and tools on drug resistance and other global health topics,
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Thank you!
Background Slides
Current Situation
• Data from China and models suggest that by the time cases are recognized there has already been transmission ongoing for weeks
• In India initial infections likely first arrived in early February
Data from Wuhan
Potentially first case in November
Projections for India
• Created using IndiaSIM, a validated, simulated population of the Indian
population based on NFHS and other national data. Previously used for
NTAGI for vaccine introduction modeling.
§ EachindividualinthissimulatedmodelrepresentsanactualIndianintermsofage,income, location, health system access and other covariates found in NFHS data.
• Why might India do better than other countries?
§ Relativelyyoungpopulation.
§ SeasonalitythatisexpectedforCOVID-19coulddelayinfectionsintolaterintheyear.
• Why might India do worse?
§ NutritionchallengesinyoungerpopulationrelativetoChinaandItalywithunknownimpacts
on children.
§ Greateropportunitiesfordiseasetransmissionanddifficultyofsocialdistancing.
Transmissibility of COVID-19
Reproduction Number (R0) –
The average number of people an infected person will infect
Based on Contact Rates –
i.e. How many people a person contacts And the probability the virus is transmitted
Best estimate is that R0 is between 2 and 3
Emerging evidence that increased temperature and humidity would reduce this to 1.8 in a month.
https://doi.org/10.1101/20 20.01.23.20018549.
Disease Timeline: Transmission without symptoms
• Disease Parameters:
§ Incubationtime:6days(SD2days)
§ Timetohospitalization:3.5days(SD1.8days)
§ DaysinHospital:12days(SD3.4days)
§ Contagioustimenon-hospital:9days(SD3days)
DOI: 10.7326/M20-0504
The dynamics are slowed –
People who will show symptoms next week are already infected and incubating the virus. Some of these will transmit before they are symptomatic.
Global Trends in Confirmed Cases
Takes nearly 2 weeks to change course of infection
Countrywide lockdowns begin in China
Model Assumptions
• Contact and Transmission depends on type of contact and with whom contact is made
• Contact patterns highly variable by age (e.g. kids have higher rates of contact1).
• Household contact rates are higher than outside contacts.
• Age-related assortative contact patterns1
Contact Rates by Age Age Assortative Contact
1. 10.1371/journal.pmed.0050074
Number of International Airline Travelers by Indian State,
January 2020
Number of International Airline Travelers by Indian State, January 2020
State
Delhi Maharashtra Kerala Tamil Nadu Karnataka Telangana West Bengal Gujarat Goa Uttar Pradesh Punjab Rajasthan Bihar Andhra Pradesh Odisha Chandigarh Madhya Pradesh Assam
1,749,594
491,042
734,655
978,571
1,266,531
372,329 286,589
223,385
107,974 85,452 83,752
50,987 23,761
13,779 7,234 5,069 3,476 2,712
0K 100K 200K 300K 400K 500K 600K 700K 800K 900K 1000K 1100K 1200K 1300K 1400K 1500K 1600K 1700K 1800K 1900K
Number of International Passengers
Temperature and humidity
Evidence from China indicates that higher temperature and humidity are likely to lower the transmission rates but it is unclear how this will translate to the India context.
Mortality higher in older individuals
A large percentage of cases are mild, but for older individuals the mortality rate is strikingly higher
Mortality higher in older individuals
In China mortality was much higher in the elderly…
…The US is similar.
Evidence on children
• Children are less likely to be infected and also less likely to be hospitalized than adults.
• Illness is less likely to be severe in children than in adults.
SEVERITY OF COVID-19 INFECTIONS IN CHILDREN
Asymp tomatic
Mild Moder ate
Severe /critical
39%
51%
6%4%
Source: Dong Y, et al. Pediatrics. March 16, 2020
Evidence on children
• Children have overall lower levels of severe or critical infection than adults. Percent of COVID-19 cases that are severe/critical
20
18
16
14
12
10
8 6 4 2 0
Infants
Age 1-5
Age 6-10
Age Group
Age 16+
Adult
Source: Dong Y, et al. Pediatrics. March 16, 2020
Age 11-15
Percent
Country comparison: South Korea vs Italy
South Korea
Italy
Population
51 M
60 M
Total testing (till March 17, 2020)
286,716
148,657
Mortality among those infected with COVID-19 (till Mar 17, 2020)
81 deaths
2,500 deaths
Death toll has surpassed China’s, becoming the country with the highest number of coronavirus deaths in the world
Death rate with COVID-19
0.7%
8%
S Korea: has been conducting around 12,000-15,000 tests every day, and has the capacity to do 20,000 daily; it has done ~ 250,000 tests till date.
Ref: 1. Population 2. Total testing 3. Business Insider
Testing protocol in South Korea
• Trace, test and treat protocol
• Early and efficient testing
• Drive-thru COVID-19 testing centers, Covid-19 testing tents, at- home testing kits
• External quality assessment services screen hospitals and health institutions before they are certified to test patients for the virus.
• Disease surveillance system is “preemptive” in that it was scanning and discovering patients early on
Testing protocol in South Korea
Test
Test category
Kit name (examples)
Company (examples)
Testing time
Accuracy
Specimen
Rapid diagnostic tests (RDTs)
Screening (antibody- based test)
COVID- 19 Ag GICA Rap id
PCL
10 min
~85%
Nasal discharge
Real-time PCR
Confirmatory (nucleic acid- based test)
Seegene, Kogene, SolGent Co, Sd Biosensor
5-6 hours to 2 days
~98%
Nasopharyngeal specimen
Ref: 1. Population 2. Total testing 3. Business Insider
WHO Interim Guidance
• Screening of Naso/oropharyngeal swab (for upper respiratory) and sputum/endotracheal aspirate/bronchoalveolar lavage (for lower respiratory infection) with nucleic acid amplification tests (NAAT), such as RT-PCR.
• Also test for other respiratory pathogens using routine laboratory procedures.
• Serological surveys: aid investigation of an ongoing outbreak and retrospective assessment of
the attack rate or extent of an outbreak.
• Viral sequencing: supports confirmation of the presence of the virus, regular sequencing of (some samples )-useful to monitor for viral genome mutations that might affect the performance of medical countermeasures, including diagnostic tests.
Source: WHO Interim Guidance 2nd March Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases.
WHO Interim Guidance | COVID-19 Lab confirmation protocol
Laboratory-confirmed by nucleic acid amplification tests (NAAT) in an area with no COVID-19 virus circulation
A positive NAAT result for at least two different targets on the COVID-19 virus genome, of which at least one target is preferably specific for COVID-19 virus using a validated assay (as at present no other SARS-like coronaviruses are circulating in the human population it can be debated whether it has to be COVID-19 or SARS- like coronavirus specific);
OR
One positive NAAT result for the presence of betacoronavirus, and COVID-19 virus further identified by sequencing partial or whole genome of the virus.
Laboratory-confirmed by NAAT in an area with established COVID-19 virus circulation
Simpler algorithm might be adopted in which e.g. screening by rRT-PCR of a single discriminatory target is considered sufficient.
One or more negative results do not rule out the possibility of COVID-19 virus infection.
Source: WHO Interim Guidance 2nd March Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases.
Surveillance networks were tapped to test COVID 19 in China
RT-PCR testing of COVID-19 virus in
• Influenza-like-illness (ILI) and
• Severe acute respiratory infection (SARI) surveillance systems
• Fever clinics
Transmission dynamics
• Transmission within health care settings and amongst health care workers does not appear to be a major feature of COVID-19 in China.
• Investigations among HCW suggest that many may have been infected within the household rather than in a health care setting.
Source: WHO Wuhan report of Joint Commission, https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
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