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The Center For Disease Dynamics, Economics & Policy

IndiaSIM Model

CDDEP will issue guidance reports on nationwide and state-level estimates of COVID-19 in India based on evidence from the IndiaSIM Model.


The Model

We use a version of IndiaSIM, a suite of models of the Indian population. Versions of IndiaSIM have been published widely over many years and have been used for government decision-making including by NTAGI for vaccine introduction. The suite of models comprises both compartmental models as well as agent-based simulations that account for the demography (age, gender) of the Indian population, and can, where appropriate examine the impacts of socio-economic characteristics and access to healthcare on disease outcomes. Here we start with simpler models of transmission that account for the biology and epidemiology of the emerging pathogen to describe potential scenarios for India that account for the tremendous uncertainty that remains in the trajectory of the disease.


The model is fit 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. We examine the potential for seasonality, based on the fact that most respiratory infections decline in the summer, however to-date evidence for the magnitude of this effect, which is driven by changes in temperature and humidity is not well described, and there is potential for the virus to continue its rapid spread.


We are continuously updating the model as new data on parameters and the Indian population become available. Future guidance reports will list these changes.



  • 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.


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 COVID-19 affected countries
  • Age and demographic variables


What did we find?

  1. Community transmission of COVID-19 in India most likely started in early March.
  2. National containment is no longer an option in India. However, state or local (temporary) containment and mitigation is the best option.
  3. At baseline (without interventions), between 300 and 400 million Indians are likely to be infected by July. Most of these cases will be mild. At the peak (somewhere between April and May 2020), 100 million individuals will be infected. Of these, approximately 10 million will be severe and about 2-4 million will require hospitalization. This is the most critical period.
  4. Generalized social distancing can, in theory, reduce this peak load by as much as 75%, although this may be difficult to enforce in India.
  5. Hospital outbreaks of COVID-19 induced by the admission of infected patients into hospitals could also be a major issue. Thus, there is a need for large, temporary hospitals to handle this patient load over the next three-month period. Secondary, hospital-based transmission fuels the epidemic.

What do we recommend?

Social distancing

  • Immediate social distancing, focused on the elderly population is essential. Anyone above the age of 65 should essentially shelter in place, while everyone else should practice significant social distancing.
  • We have modeled a three-week period of complete isolation for the elderly. The longer this period, the more we can delay infections into the post-July period.
  • We have assumed high compliance with non-elderly social distancing in our model. Even if moderate, this may be the most useful option given where we are in the epidemic, in order to contain the peak.


Containment is not an option. However, testing is essential for the purpose of identifying COVID-19 cases, especially in the elderly and the under-five population, preventing local hotspots of transmission, separating patients in healthcare facilities to keep infected and uninfected patients apart, and for tracking the course of the epidemic. It may be difficult to scale RT-PCR for the general population.

  • Test anyone with flu-like symptoms who approaches the healthcare system to ensure that COVID-19 cases do not mix with the general hospitalized population. An epidemic in healthcare institutions, both public and private, would amplify transmission and epidemic peaks.
  • Expand testing to private labs immediately.
  • Early, at home testing for anyone over the age of 60 who report these symptoms should be made available for early access to care. This could limit their exposure to healthcare facilities. Where possible, a separate cohort of trained lab technicians would need to be developed. Where these are not present, existing ANMs should be trained in infection control and deployed. A rapid assay should be used rather than RT-PCR. There is a risk of over-identifying cases in this population but that is preferable to under-identification. All patients should ideally be tested under RT-PCR before being admitted to the facility, lest we expose them to COVID-19 inadvertently. This may not be possible in all locations, but the benefit of over-identification and early admission to care would outweigh delays in care-seeking.
  • Nationally, testing will require a multi-pronged approach—which means implementing both RT- PCR and assay-based rapid tests, which were used in South Korea.


  • Current ICU- and ventilator-equipped bed capacity in India is wholly inadequate to the number of infections at epidemic peak. Current capacity is estimated at 30-50K ventilators nationwide and about 70-100K ICU beds. We are awaiting better estimates from the government, but is likely to need expansion immediately. We will need upwards of 700,000 and as many as one million ventilators to address the peak. An alternative may be to use tracheostomy and lower-cost ventilators. Supplementary oxygen is essential. Suction can be done using closed suction devices where infection control is better done without PPEs.
  • Oxygen and non-invasive positive pressure ventilation (e.g CPAP) are needed. This capacity is inadequate outside of tier 1 metro hospitals.
  • Given the high risk of infections and low levels of infection control in hospitals including in urban areas, it is important to avoid admitting COVID-19 patients to regular facilities. We need to consider how much care can be delivered at home, but it is important to note that CPAP will generate aerosols. See our point on testing above.
  • Given the significant peak of infections in the next 4-10 weeks and the likelihood of a recurrence in November/December, plans should be made for temporary treatment facilities across India, focused on areas where severe cases are most likely to reach by July. The lack of adequate testing data makes it difficult to adequately model this at this time.
  • 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. Healthcare staff cohorting and upscaling hospital infection control in all NHA facilities are essential. Without infection control, healthcare workers get sick, further straining the capacity of the healthcare system.


  • Reliable data on caseloads would help us with data on targeting resources proactively. At this point, all cases of flu-like illness should be tested for COVID-19 in both inpatient and outpatient settings, at least in tier 1 cities and expanding further.
  • Immediate and continuing state-level representative, serological surveys are needed to monitor the stage of the epidemic. The government has now permitted notified DBT research labs to begin this work, and this should be expedited.
  • There should be 1 or 2 centers in the country where a larger number of COVID-19 patients are expected, and where there is research capacity, to be supported for more detailed and systematic data collection including understanding clinical course in the Indian context, viral shedding (both NP and stool—which is documented, and a particular concern in the south Asian context), and pediatric illness in the context of malnourished children. The data from China of about 2000 children with COVID-19 indicated that 20-30% (including those 1-5 yr old) do have severe or critical illness. The total number of kids is small compared to the total, but children can get sick and are at greater risk in India.
  • We have assumed no treatment options in this model. The validation of antiretrovirals immediately could help reduce the mortality rate among the elderly population.
  • We have assumed that no vaccination options will be developed within the next 12-month period.


Summary for Policy

  • Delays in testing are seriously reducing the ability of the population to self—protect. 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.
  • 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.
  • The national lockdown will delay things, but will not reduce the overall numbers greatly in the long-term. Though this will cause serious economic damage, increase hunger and reduce the population resilience for handling the infection peak, it does buy time to invest in preparedness now, including producing ventilators and building hospital capacity.
  • Continued regional lockdowns are likely needed as the epidemic will progress from the more populous states to less populated states. 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 provide time for the government to prepare for the projected onslaught of cases 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.
  • Preparedness for caseload 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.
  • Temperature and humidity increases may help us in reducing caseload, but given the likely widespread number of cases, should not be counted on to reduce the short-term impacts.
  •  Current estimates suggest that the elderly are most susceptible and children have not been particularly affected, but given the levels of malnutrition in the population, this situation should be monitored closely. Early testing and healthcare in these populations could help to significantly reduce the mortality toll of the epidemic.
  • 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.


*Our COVID-19 modeling estimates for India were produced by a team of researchers affiliated with CDDEP, Johns Hopkins, and Princeton. As with all academic work, this does not reflect the official views of CDDEP, Johns Hopkins University or Princeton University.


Eili Klein (Assistant Professor, Department of Emergency Medicine, Johns Hopkins School of Medicine), Gary Lin (Post-doctoral fellow, Department of Emergency Medicine, Johns Hopkins School of Medicine), Katie Tseng (CDDEP), Emily Schueller (CDDEP), Geetanjali Kapoor (CDDEP), Ramanan Laxminarayan (CDDEP; Senior Associate, Johns Hopkins Bloomberg School of Public Health; Senior Research Scholar, Princeton University).